医疗人工智能方向论文汇总思维导图

U-Net: Convolutional Networks for Biomedical Image Segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Attention U-Net: Learning Where to Look for the Pancreas

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Brain Tumor Segmentation with Deep Neural Networks

UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

UNETR: Transformers for 3D Medical Image Segmentation

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

ResUNet++: An Advanced Architecture for Medical Image Segmentation

nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Pyramid Scene Parsing Network

Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)

Road Extraction by Deep Residual U-Net

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

Boundary loss for highly unbalanced segmentation

Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

RECOD Titans at ISIC Challenge 2017

Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

Acute and sub-acute stroke lesion segmentation from multimodal MRI

Attention U-Net: Learning Where to Look for the Pancreas

Brain Tumor Segmentation with Deep Neural Networks

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

3D MRI brain tumor segmentation using autoencoder regularization

Autofocus Layer for Semantic Segmentation

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation

TBraTS: Trusted Brain Tumor Segmentation

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

CNN-based Segmentation of Medical Imaging Data

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

MRI Super-Resolution using Multi-Channel Total Variation

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

A Learning Strategy for Contrast-agnostic MRI Segmentation

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

Deep Neural Networks for Anatomical Brain Segmentation

Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data

Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control

3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles

Project & Excite' Modules for Segmentation of Volumetric Medical Scans

Importance Driven Continual Learning for Segmentation Across Domains

U-Net: Convolutional Networks for Biomedical Image Segmentation

Deep Learning in Single-Cell Analysis

CE-Net: Context Encoder Network for 2D Medical Image Segmentation

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders

Applying Faster R-CNN for Object Detection on Malaria Images

Cell Detection with Star-convex Polygons

Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency

Automatic Label Correction for the Accurate Edge Detection of Overlapping Cervical Cells

Microscopy Cell Segmentation via Adversarial Neural Networks

Microscopy Cell Segmentation via Convolutional LSTM Networks

Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario

Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement

Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes

DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

RECOD Titans at ISIC Challenge 2017

Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

Automatic skin lesion segmentation with fully convolutional-deconvolutional networks

Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation

Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images

Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks

Automatic Skin Lesion Segmentation Using GrabCut in HSV Colour Space

Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

Road Extraction by Deep Residual U-Net

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Dynamic Deep Networks for Retinal Vessel Segmentation

LadderNet: Multi-path networks based on U-Net for medical image segmentation

Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

Accurate Retinal Vessel Segmentation via Octave Convolution Neural Network

Retinal Vessel Segmentation based on Fully Convolutional Networks

IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks

Channel Attention Residual U-Net for Retinal Vessel Segmentation

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation

RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network

Learning to Address Intra-segment Misclassification in Retinal Imaging

Retinal Image Restoration and Vessel Segmentation using Modified Cycle-CBAM and CBAM-UNet

007: Democratically Finding The Cause of Packet Drops

Acute and sub-acute stroke lesion segmentation from multimodal MRI

A Learning Strategy for Contrast-agnostic MRI Segmentation

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

3D Densely Convolutional Networks for Volumetric Segmentation

3D Densely Convolutional Networks for VolumetricSegmentation

Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

Cardiac MRI Segmentation with Strong Anatomical Guarantees

Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation

Distributionally Robust Deep Learning using Hardness Weighted Sampling

Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains

PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation

1.BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

2.UNETR: Transformers for 3D Medical Image Segmentation

3.On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

4.Med3D: Transfer Learning for 3D Medical Image Analysis

5.3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study

6.Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

7.Enforcing temporal consistency in Deep Learning segmentation of brain MR images

8.Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

9.Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation

10.AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy

11.Elastic Boundary Projection for 3D Medical Image Segmentation

12.Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

13.Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation

14.3D Densely Convolutional Networks for VolumetricSegmentation

15.TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions

1.TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

2.Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

3.A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI

4.Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation

5.PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

6.Disentangle, align and fuse for multimodal and semi-supervised image segmentation

7.Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation

8.3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation

9.Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

10.Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow

11.Cardiac MRI Segmentation with Strong Anatomical Guarantees

12.An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image

13.L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI

14.Cardiac Segmentation with Strong Anatomical Guarantees

15.Learning Directional Feature Maps for Cardiac MRI Segmentation

1.The Liver Tumor Segmentation Benchmark (LiTS)

2.Med3D: Transfer Learning for 3D Medical Image Analysis

3.Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

4.Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

5.H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

6.Imperfect Segmentation Labels: How Much Do They Matter?

7.Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

8.Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function

9.Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations

10.Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation

11.Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation

12.Liver segmentation and metastases detection in MR images using convolutional neural networks

13.KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

14.Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation

15.Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study

1.Mutual Consistency Learning for Semi-supervised Medical Image Segmentation

2.MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels

3.Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation

4.When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation

5.Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

6.A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

7.Semi-supervised Medical Image Segmentation through Dual-task Consistency

8.A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision

9.Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation

10.Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels

11.Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation

12.All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

13.Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation

14.Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer

15.An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation

1.FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

2.Non-local U-Net for Biomedical Image Segmentation

3.Enforcing temporal consistency in Deep Learning segmentation of brain MR images

4.NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

5.Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

6.Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

7.ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging

8.Using deep convolutional neural networks for neonatal brain image segmentation

9.A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

10.A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

11.DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation

12.An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation

13.Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images

14.Learning from imperfect training data using a robust loss function: application to brain image segmentation

1.Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT

2.Open Source Iris Recognition Hardware and Software with Presentation Attack Detection

3.Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks

4.Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples

5.Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation

6.Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework

7.Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features

8.A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks

9.Boltzmann Exploration Expectation–Maximisation

10.Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition

11.KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation

12.Saliency-Guided Textured Contact Lens-Aware Iris Recognition

13.Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation

1.V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

2.On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

3.Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation

4.nnFormer: Interleaved Transformer for Volumetric Segmentation

5.3D Densely Convolutional Networks for VolumetricSegmentation

6.KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

7.Positional Contrastive Learning for Volumetric Medical Image Segmentation

8.A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

9.Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images

10.Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation

11.Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation

12.AdaWAC: Adaptively Weighted Augmentation Consistency Regularization for Volumetric Medical Image Segmentation

1.U-Net: Convolutional Networks for Biomedical Image Segmentation

2.Attention U-Net: Learning Where to Look for the Pancreas

3.A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

4.Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

5.U-Net Fixed-Point Quantization for Medical Image Segmentation

6.TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions

7.AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

8.Inter-slice Context Residual Learning for 3D Medical Image Segmentation

9.Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation

10.Hierarchical 3D Feature Learning for Pancreas Segmentation

11.Dynamic Linear Transformer for 3D Biomedical Image Segmentation

12.Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation

1.U-Net: Convolutional Networks for Biomedical Image Segmentation

2.UNet++: A Nested U-Net Architecture for Medical Image Segmentation

3.PraNet: Parallel Reverse Attention Network for Polyp Segmentation

4.Video Polyp Segmentation: A Deep Learning Perspective

5.Progressively Normalized Self-Attention Network for Video Polyp Segmentation

6.See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks

7.MATNet: Motion-Attentive Transition Network for Zero-Shot Video Object Segmentation

8.Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection

9.Shallow Attention Network for Polyp Segmentation

10.Full-Duplex Strategy for Video Object Segmentation

11.The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

1.Road Extraction by Deep Residual U-Net

2.Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

3.Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

4.Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

5.Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

6.iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network

7.Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation

8.U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation

1.U-Net: Convolutional Networks for Biomedical Image Segmentation

2.Road Extraction by Deep Residual U-Net

3.Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

4.DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

5.FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation

6.Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks

7.Skin Lesion Segmentation using SegNet with Binary Cross-Entropy

8.Training on Polar Image Transformations Improves Biomedical Image Segmentation

1.SGDR: Stochastic Gradient Descent with Warm Restarts

2.Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

3.EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

4.DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

5.Deep learning with convolutional neural networks for EEG decoding and visualization

6.U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

7.An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

8.Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs

9.Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

10.Deep learning-based electroencephalography analysis: a systematic review

11.SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

12.SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

13.Advancing NLP with Cognitive Language Processing Signals

14.An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

15.Using Riemannian geometry for SSVEP-based Brain Computer Interface

1.Deep learning with convolutional neural networks for EEG decoding and visualization

2.Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

3.Decoding P300 Variability using Convolutional Neural Networks

4.Subject-Aware Contrastive Learning for Biosignals

5.Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional neural networks

6.Uncertainty Detection and Reduction in Neural Decoding of EEG Signals

7.ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation

8.Physics-inform attention temporal convolutional network for EEG-based motor imagery classification

9.MAtt: A Manifold Attention Network for EEG Decoding

10.fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships

11.Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation

1.EEGdenoiseNet: A benchmark dataset for end-to-end deep learning solutions of EEG denoising

2.Improved robust weighted averaging for event-related potentials in EEG

3.Embedding Decomposition for Artifacts Removal in EEG Signals

1.Semi-Supervised Classification with Graph Convolutional Networks

2.Neural Message Passing for Quantum Chemistry

3.Gated Graph Sequence Neural Networks

4.Junction Tree Variational Autoencoder for Molecular Graph Generation

5.Self-Normalizing Neural Networks

6.Convolutional Networks on Graphs for Learning Molecular Fingerprints

7.PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges

8.Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

9.Molecule Attention Transformer

10.Directional Message Passing for Molecular Graphs

11.An Overview of Multi-Task Learning in Deep Neural Networks

12.DeepDTA: Deep Drug-Target Binding Affinity Prediction

13.Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

14.Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity

15.ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? 28 Aug 2017 · Mostapha Benhenda · Edit social preview

1.Variational Autoencoder for Anti-Cancer Drug Response Prediction

2.Learning Curves for Drug Response Prediction in Cancer Cell Lines

3.ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs

4.Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

5.A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction

6.Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms

7.TransEDRP: Dual Transformer model with Edge Emdedded for Drug Respond Prediction

1.Multitask learning and benchmarking with clinical time series data

2.Efficient nonparametric statistical inference on population feature importance using Shapley values

3.MIMIC-III, a freely accessible critical care database

4.Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care

5.Using Clinical Notes with Time Series Data for ICU Management

6.Benchmarking machine learning models on multi-centre eICU critical care dataset

7.Neural Document Embeddings for Intensive Care Patient Mortality Prediction

8.Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets

9.Early hospital mortality prediction using vital signals

10.Countdown Regression: Sharp and Calibrated Survival Predictions

11.Interpretable Patient Mortality Prediction with Multi-value Rule Sets

12.Dynamic Measurement Scheduling for Event Forecasting using Deep RL

13.Knowledge-based Analysis for Mortality Prediction from CT Images

14.Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data

15.Evaluation of Embeddings of Laboratory Test Codes for Patients at a Cancer Center

1.Deep Learning for ECG Classification

2.Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings

3.Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification

4.Voice2Series: Reprogramming Acoustic Models for Time Series Classification

5.ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks

6.A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification

7.Automatic diagnosis of the 12-lead ECG using a deep neural network

8.Diffeomorphic Temporal Alignment Nets

9.Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length

10.ECG beats classification via online sparse dictionary and time pyramid matching

11.Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM

12.Classification of 12-lead ECGs: the PhysioNet/ Computing in Cardiology Challenge 2020

13.Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs

14.Self-supervised representation learning from 12-lead ECG data

15.A practical system based on CNN-BLSTM network for accurate classification of ECG heartbeats of MIT-BIH imbalanced dataset

1.ECG Heartbeat Classification: A Deep Transferable Representation

2.ECG arrhythmia classification using a 2-D convolutional neural network

3.Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

4.An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

5.Deep Learning for ECG Classification

6.Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

7.Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings

8.Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification

9.Blind ECG Restoration by Operational Cycle-GANs

10.Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG

11.Real time P, QRS and T wave detection by QRS matched filter method

12.VFPred: A Fusion of Signal Processing and Machine Learning techniques in Detecting Ventricular Fibrillation from ECG Signals

13.Arrhythmia Detection Using Deep Convolutional Neural Network With Long Duration ECG Signals

14.A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification

15.ECG Segmentation by Neural Networks: Errors and Correction

1.ECG Heartbeat Classification: A Deep Transferable Representation

2.ECG arrhythmia classification using a 2-D convolutional neural network

3.Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

4.Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach

5.An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

6.Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings

7.Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG

8.ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks

9.VFPred: A Fusion of Signal Processing and Machine Learning techniques in Detecting Ventricular Fibrillation from ECG Signals

10.Arrhythmia Detection Using Deep Convolutional Neural Network With Long Duration ECG Signals

11.Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model

12.ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network

13.ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks

14.ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networks

15.End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices

1.Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices

2.Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks

3.SCAMPS: Synthetics for Camera Measurement of Physiological Signals

4.Specific Differential Entropy Rate Estimation for Continuous-Valued Time Series

5.Sleep quality prediction in caregivers using physiological signals

6.A persistent homology approach to heart rate variability analysis with an application to sleep-wake classification

7.RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG

8.Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling

9.Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error

10.PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression

11.Video-based Remote Physiological Measurement via Self-supervised Learning

12.Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals

13.CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals

1.ECG Heartbeat Classification: A Deep Transferable Representation

2.Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach

3.Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers

4.Construe: a software solution for the explanation-based interpretation of time series

5.ATCN: Resource-Efficient Processing of Time Series on Edge

6.ECG Heartbeat Classification Using Multimodal Fusion

1.Blind ECG Restoration by Operational Cycle-GANs

2.Hierarchical Filtering with Online Learned Priors for ECG Denoising

1.ECG Heartbeat Classification: A Deep Transferable Representation

2.Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms

1.BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

2.Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

3.DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks

4.A Benchmark of Medical Out of Distribution Detection

5.Multi-layer Representation Learning for Medical Concepts

6.High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

7.TCAV: Relative concept importance testing with Linear Concept Activation Vectors

8.Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

9.D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation

10.UnMask: Adversarial Detection and Defense Through Robust Feature Alignment

11.Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans

12.SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data

13.Patient Embeddings in Healthcare and Insurance Applications

14.RuMedBench: A Russian Medical Language Understanding Benchmark

15.Reasoning with Language Model Prompting: A Survey

1.Deep Residual Learning for Image Recognition

2.MobileNetV2: Inverted Residuals and Linear Bottlenecks

3.Rethinking the Inception Architecture for Computer Vision

4.Xception: Deep Learning With Depthwise Separable Convolutions

5.Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images

6.Retinal OCT disease classification with variational autoencoder regularization

7.Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images

8.Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

1.The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

2.The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech

3.Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans

4.Convolutional neural networks for Alzheimer’s disease detection on MRI images

5. MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation

6.Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection

1.Machine learning approach of automatic identification and counting of blood cells

2.AIDeveloper: deep learning image classification in life science and beyond

1.CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

2.Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain

1.Deep Residual Learning for Image Recognition

2.Densely Connected Convolutional Networks

3.EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

4.Res2Net: A New Multi-scale Backbone Architecture

5.Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

6.ResNet strikes back: An improved training procedure in timm

7.Contrastive Learning of Medical Visual Representations from Paired Images and Text

8.Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

9.RegNet: Self-Regulated Network for Image Classification

10.Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

11.Tensor Networks for Medical Image Classification

12.Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction

13.Synthetic Medical Images from Dual Generative Adversarial Networks

14.Capsule Networks against Medical Imaging Data Challenges

15.Evolutionary Neural AutoML for Deep Learning

1.Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

2.In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

3.Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification

4.Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

5.ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

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1.VoxelMorph: A Learning Framework for Deformable Medical Image Registration

2.Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network

3.Recursive Cascaded Networks for Unsupervised Medical Image Registration

4.An Unsupervised Learning Model for Deformable Medical Image Registration

5.AirLab: Autograd Image Registration Laboratory

6.Networks for Joint Affine and Non-parametric Image Registration

7.Enhancing Medical Image Registration via Appearance Adjustment Networks

8.GRADICON：: Approximate Diffeomorphisms via Gradient Inverse Consistency

9.Deformable medical image registration: setting the state of the art with discrete methods

10.An Artificial Agent for Robust Image Registration

11.Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information

12.Benchmarking of image registration methods for differently stained histological slides

13.FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registration

14.Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

15.Recursive Cascaded Networks for Unsupervised Medical Image Registration

1.VoxelMorph: A Learning Framework for Deformable Medical Image Registration

2.Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

3.Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

4.Metric Learning for Image Registration

5.Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation

6.Reliable brain morphometry from contrast‐enhanced T1w‐MRI in patients with multiple sclerosis

1.Consistent and elastic registration of histological sections using vector-spline regularization

2.Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

3.Deformable medical image registration: setting the state of the art with discrete methods

4.Benchmarking of image registration methods for differently stained histological slides

5.BIRL: Benchmark on Image Registration methods with Landmark validation

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1.BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

2.Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

3.Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

4.High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

5.Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

6.Detecting and classifying lesions in mammograms with Deep Learning

7.On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

8.Conditional Infilling GANs for Data Augmentation in Mammogram Classification

9.Regression Concept Vectors for Bidirectional Explanations in Histopathology

10.Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer

11.An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

12.Differences between human and machine perception in medical diagnosis

13.Curvature-based Feature Selection with Application in Classifying Electronic Health Records

14.O2PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection

15.Machine Learning-Based Approaches For Breast Cancer Detection in Microwave Imaging

1.Knowledge Transfer for Melanoma Screening with Deep Learning

2.Skin Lesion Synthesis with Generative Adversarial Networks

3.Data Augmentation for Skin Lesion Analysis

4.Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks

5.Deep neural network or dermatologist?

6.Melanoma Detection using Adversarial Training and Deep Transfer Learning

7.Convolutional Neural Networks for Classifying Melanoma Images

8.Model Patching: Closing the Subgroup Performance Gap with Data Augmentation

9.Semi-Supervised Federated Peer Learning for Skin Lesion Classification

10.Soft-Attention Improves Skin Cancer Classification Performance

11.Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

12.Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

1.Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

2.Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

3.Regression Concept Vectors for Bidirectional Explanations in Histopathology

4.Magnification Generalization for Histopathology Image Embedding

5.VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset

6.BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix

1.Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses

2.Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

3.Synthetic Lung Nodule 3D Image Generation Using Autoencoders

4.Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

5.Knowledge-based Analysis for Mortality Prediction from CT Images

6.Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography

1.Multitask learning and benchmarking with clinical time series data

2.Efficient nonparametric statistical inference on population feature importance using Shapley values

3.MIMIC-III, a freely accessible critical care database

4.Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care

5.Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care

6.Using Clinical Notes with Time Series Data for ICU Management

7.Benchmarking machine learning models on multi-centre eICU critical care dataset

8.Neural Document Embeddings for Intensive Care Patient Mortality Prediction

9.Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets

10.Early hospital mortality prediction using vital signals

11.Countdown Regression: Sharp and Calibrated Survival Predictions

12.Interpretable Patient Mortality Prediction with Multi-value Rule Sets

13.Dynamic Measurement Scheduling for Event Forecasting using Deep RL

14.Knowledge-based Analysis for Mortality Prediction from CT Images

15.Analysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data

1.Dynamic Measurement Scheduling for Event Forecasting using Deep RL

2.ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes

3.HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data

4.FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction

5.Early prediction of the risk of ICU mortality with Deep Federated Learning

1.A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

2.The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

3.Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

4.Naive Bayes and Text Classification I - Introduction and Theory

5.VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers

6.Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions

7.Heart Disease Prediction System using Associative Classification and Genetic Algorithm

8.Spectral Graph Convolutions for Population-based Disease Prediction

9.Robust Multiple Kernel k-means Clustering using Min-Max Optimization

10.Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease

11.Deep EHR: Chronic Disease Prediction Using Medical Notes

12.TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease

13.MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare

14.An overview of deep learning in medical imaging focusing on MRI

15.Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System

1.RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism

2.CLIMAT: Clinically-Inspired Multi-Agent Transformers for Knee Osteoarthritis Trajectory Forecasting

3.Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes

4.Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data

1.DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

2.SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

3.An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

4.SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

5.Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG

6.Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG

7.Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging

8.Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

9.Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

10.REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild

11.An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

12.ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

13.A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging

14.Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring

1.DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal

2.Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

3.An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

4.Quantified Sleep: Machine learning techniques for observational n-of-1 studies

5.MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor

6.Advanced sleep spindle identification with neural networks

7.DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning

8.A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data

1.DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal

2.Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning

3.RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection

4.Advanced sleep spindle identification with neural networks

5.DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning

1.DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal

2.Non-contact Infant Sleep Apnea Detection

3.Learning Realistic Patterns from Unrealistic Stimuli: Generalization and Data Anonymization

4.A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors

5.Sleep Apnea Detection From Single-Lead ECG: A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms

1.Highly accurate protein structure prediction with AlphaFold

2.MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

3.Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

4.SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning

5.ProteinNet: a standardized data set for machine learning of protein structure

6.Distribution-Free, Risk-Controlling Prediction Sets

7.Iterative SE(3)-Transformers

8.HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle

9.Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction

10.How pairwise coevolutionary models capture the collective residue variability in proteins

11.Universal Transforming Geometric Network

12.PolyFold: an interactive visual simulator for distance-based protein folding

13.Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder

14.Energy-based models for atomic-resolution protein conformations

15.FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

1.Protein Interface Prediction using Graph Convolutional Networks

2.End-to-End Learning on 3D Protein Structure for Interface Prediction

3.DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction

4.When Geometric Deep Learning Meets Pretrained Protein Language Models

1.Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer

2.sEMG Gesture Recognition with a Simple Model of Attention

3.Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals

4.EV-Action: Electromyography-Vision Multi-Modal Action Dataset

5.Parkinson’s Disease EMG Signal Prediction Using Neural Networks

6.Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

7.Digital Voicing of Silent Speech

8.End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning

9.An Improved Model for Voicing Silent Speech

10.Efficient TMS-Based Motor Cortex Mapping Using Gaussian Process Active Learning

11.Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation

12.neuro2vec: Masked Fourier Spectrum Prediction for Neurophysiological Representation Learning

13.Dreamento: an open-source dream engineering toolbox for sleep EEG wearables

1.Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

2.sEMG Gesture Recognition with a Simple Model of Attention

3.An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier

4.Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks

5.A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition

6.Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

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