MEDICAL IMAGING MEETS NeurIPS
An official NeurIPS Workshop - December 2020 - Online-only
An official NeurIPS Workshop - December 2020 - Online-only
02:20 AM Welcome & Opening Remarks
02:30 AM Keynote I: Addressing the Data Bottleneck in Biomedical Image Analysis – Lena Maier-Hein (DKFZ)
03:10 AM Oral Session I
3D Infant Pose Estimation Using Transfer Learning
3D UNet with GAN discriminator for robust localisation of the fetal brain and trunk in MRI with partial coverage of the fetal body
A Bayesian Unsupervised Deep-Learning Based Approach for Deformable Image Registration
A Critic Evaluation Of Covid-19 Automatic Detection From X-Ray Images
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation
Annotation-Efficient Deep Semi-Supervised Learning for Automatic Knee Osteoarthritis Severity Diagnosis from Plain Radiographs
Biomechanical modelling of brain atrophy through deep learning
Brain2Word: Improving Brain Decoding Methods and Evaluation
Classification with a domain shift in medical imaging
Decoding Brain States: Clustering fMRI Dynamic Functional Connectivity Timeseries with Deep Autoencoders
DeepSim: Semantic similarity metrics for learned image registration
Diffusion MRI-based structural connectivity robustly predicts "brain-age''
Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI
Improving the Interpretability in Medical Imaging Diagnosis using Adversarial Training
Joint Hierarchical Bayesian Learning of Full-structure Noise for Brain Source Imaging
Privacy-preserving medical image analysis
Quantification of task similarity for efficient knowledge transfer in biomedical image analysis
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks
Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning
Scalable solutions for MR image classification of Alzheimer's disease
Self-supervised out-of-distribution detection in brain CT scans
Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures
Semi-Supervised Learning of MR Image Synthesis without Fully-Sampled Ground-Truth Acquisitions
Towards disease-aware image editing of chest X-rays
Unsupervised detection of Hypoplastic Left Heart Syndrome in fetal screening
Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images
05:00 AM Keynote II: Real-world Insights from Patient-facing Machine Learning Models – Nathan Silberman (Butterfly Network)
05:40 AM Oral Session II
05:40 AM – Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images – Kathryn Schutte
05:50 AM – Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network – Li Sun
06:45 AM Keynote III: The Federated Tumor Segmentation (FeTS) Initiative: Towards a paradigm-shift in multi-institutional collaborations – Spyros Bakas (University of Pennsylvania)
07:25 AM Oral Session III
A Deep Learning Model to Detect Anemia from Echocardiography
AI system for predicting the deterioration of COVID-19 patients in the emergency department
Attention Transfer Outperforms Transfer Learning in Medical Image Disease Classifiers
Autoencoder Image Compression Algorithm for Reduction of Resource Requirements
Can We Learn to Explain Chest X-Rays?: A Cardiomegaly Use Case
Clinical Validation of Machine Learning Algorithm Generated Images
Community Detection in Medical Image Datasets: Using Wavelets and Spectral Clustering
Comparing Sparse and Deep Neural Network(NN)s: Using AI to Detect Cancer.
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
COVIDNet-S: SARS-CoV-2 lung disease severity grading of chest X-rays using deep convolutional neural networks
Deep learning to assist radiologists in breast cancer diagnosis with ultrasound imaging
Embracing the Disharmony in Heterogeneous Medical Data
Harmonization and the Worst Scanner Syndrome
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
Hip Fracture Risk Modeling Using DXA and Deep Learning
Learning MRI contrast agnostic registration
Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation
LVHNet: Detecting Cardiac Structural Abnormalities with Chest X-Rays
Modified VGG16 Network for Medical Image Analysis
Multi-Label Incremental Few-Shot Learning for Medical Image Pathology classifiers
MVD-Fuse: Detection of White Matter Degeneration via Multi-View Learning of Diffusion Microstructure
Predicting the Need for Intensive Care for COVID-19 Patients using Deep Learning on Chest Radiography
Probabilistic Recovery of Missing Phase Images in Contrast-Enhanced CT
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
StND: Streamline-based Non-rigid partial-Deformation Tractography Registration
Ultrasound Diagnosis of COVID-19: Robustness and Explainability
Zero-dose PET Reconstruction with Missing Input by U-Net with Attention Modules
09:00 AM Keynote IV: New Approaches for Magnetic Resonance Image Harmonization – Jerry Prince (Johns Hopkins University)
09:40 AM Oral Session IV
09:40 AM – Brain2Word: Improving Brain Decoding Methods and Evaluation – Damian Pascual Ortiz
09:50 AM – 3D Infant Pose Estimation Using Transfer Learning – Simon Ellershaw
10:00 AM FastMRI Challenge
Introduction, Matthew J Muckley
Three talks from best performing teams
Keynote V: Fast(er) MRI: A Radiologist's Perspective – Yvonne W. Lui (NYU Langone Health)