( events) Timezone: America/Los_Angeles
'Medical Imaging meets NeurIPS' is a satellite workshop established in 2017. The workshop aims to bring researchers together from the medical image computing and machine learning communities. The objective is to discuss the major challenges in the field and opportunities for joining forces. This year the workshop will feature online oral and poster sessions with an emphasis on audience interactions. In addition, there will be a series of high-profile invited speakers from industry, academia, engineering and medical sciences giving an overview of recent advances, challenges, latest technology and efforts for sharing clinical data.
Medical imaging is facing a major crisis with an ever increasing complexity and volume of data and immense economic pressure. The interpretation of medical images pushes human abilities to the limit with the risk that critical patterns of disease go undetected. Machine learning has emerged as a key technology for developing novel tools in computer aided diagnosis, therapy and intervention. Still, progress is slow compared to other fields of visual recognition which is mainly due to the domain complexity and constraints in clinical applications which require most robust, accurate, and reliable solutions. The workshop aims to raise the awareness of the unmet needs in machine learning for successful applications in medical imaging.
| Introduction by Ben Glocker (Introduction) | |
| Keynote by Lena Maier-Hein: Addressing the Data Bottleneck in Biomedical Image Analysis (Keynote) | |
| DeepSim: Semantic similarity metrics for learned image registration (Contributed Talk) | |
| Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks (Contributed Talk) | |
| Poster Session 1 (Poster Sessions) | |
| Keynote by Nathan Silberman: Real-world Insights from Patient-facing Machine Learning Models (Keynote) | |
| Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images (Contributed Talk) | |
| Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network (Contributed Talk) | |
| Break | |
| Keynote by Spyridon Bakas: The Federated Tumor Segmentation (FeTS) Initiative: Towards a paradigm-shift in multi-institutional collaborations (Keynote) | |
| Deep learning to assist radiologists in breast cancer diagnosis with ultrasound imaging (Contributed Talk) | |
| Privacy-preserving medical image analysis (Contributed Talk) | |
| Poster Session 2 (Poster Sessions) | |
| Keynote by Jerry Prince: New Approaches for Magnetic Resonance Image Harmonization (Keynote) | |
| Brain2Word: Improving Brain Decoding Methods and Evaluation (Contributed Talk) | |
| 3D Infant Pose Estimation Using Transfer Learning (Contributed Talk) | |
| FastMRI Introduction (Introduction) | |
| FastMRI Talk 1 (Contributed Talk) | |
| FastMRI Talk 2 (Contributed Talk) | |
| FastMRI Talk 3 (Contributed Talk) | |
| FastMRI keynote Yvonne Lui: Fast(er) MRI: a radiologist's perspective (Keynote) | |
| Closing remarks (Closing) | |
| 3D UNet with GAN discriminator for robust localisation of the fetal brain and trunk in MRI with partial coverage of the fetal body (Poster) | |
| Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures (Poster) | |
| Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN (Poster) | |
| Autoencoder Image Compression Algorithm for Reduction of Resource Requirements (Poster) | |
| Decoding Brain States: Clustering fMRI Dynamic Functional Connectivity Timeseries with Deep Autoencoders (Poster) | |
| RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting (Poster) | |
| A Deep Learning Model to Detect Anemia from Echocardiography (Poster) | |
| Semi-Supervised Learning of MR Image Synthesis without Fully-Sampled Ground-Truth Acquisitions (Poster) | |
| MVD-Fuse: Detection of White Matter Degeneration via Multi-View Learning of Diffusion Microstructure (Poster) | |
| Joint Hierarchical Bayesian Learning of Full-structure Noise for Brain Source Imaging (Poster) | |
| Embracing the Disharmony in Heterogeneous Medical Data (Poster) | |
| Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning (Poster) | |
| Towards disease-aware image editing of chest X-rays (Poster) | |
| Classification with a domain shift in medical imaging (Poster) | |
| Comparing Sparse and Deep Neural Network(NN)s: Using AI to Detect Cancer. (Poster) | |
| Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI (Poster) | |
| Community Detection in Medical Image Datasets: Using Wavelets and Spectral Clustering (Poster) | |
| Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation (Poster) | |
| Modified VGG16 Network for Medical Image Analysis (Poster) | |
| AI system for predicting the deterioration of COVID-19 patients in the emergency department (Poster) | |
| COVIDNet-S: SARS-CoV-2 lung disease severity grading of chest X-rays using deep convolutional neural networks (Poster) | |
| Learning MRI contrast agnostic registration (Poster) | |
| RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray (Poster) | |
| Unsupervised detection of Hypoplastic Left Heart Syndrome in fetal screening (Poster) | |
| Zero-dose PET Reconstruction with Missing Input by U-Net with Attention Modules (Poster) | |
| Diffusion MRI-based structural connectivity robustly predicts "brain-age'' (Poster) | |
| Can We Learn to Explain Chest X-Rays?: A Cardiomegaly Use Case (Poster) | |
| Biomechanical modelling of brain atrophy through deep learning (Poster) | |
| A Critic Evaluation Of Covid-19 Automatic Detection From X-Ray Images (Poster) | |
| Deep Learning extracts novel MRI biomarkers for Alzheimer’s disease progression (Poster) | |
| Clinical Validation of Machine Learning Algorithm Generated Images (Poster) | |
| Multi-Label Incremental Few-Shot Learning for Medical Image Pathology classifiers (Poster) | |
| Probabilistic Recovery of Missing Phase Images in Contrast-Enhanced CT (Poster) | |
| Predicting the Need for Intensive Care for COVID-19 Patients using Deep Learning on Chest Radiography (Poster) | |
| Ultrasound Diagnosis of COVID-19: Robustness and Explainability (Poster) | |
| Annotation-Efficient Deep Semi-Supervised Learning for Automatic Knee Osteoarthritis Severity Diagnosis from Plain Radiographs (Poster) | |
| Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation (Poster) | |
| Quantification of task similarity for efficient knowledge transfer in biomedical image analysis (Poster) | |
| Harmonization and the Worst Scanner Syndrome (Poster) | |
| Self-supervised out-of-distribution detection in brain CT scans (Poster) | |
| Attention Transfer Outperforms Transfer Learning in Medical Image Disease Classifiers (Poster) | |
| Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training (Poster) | |
| LVHNet: Detecting Cardiac Structural Abnormalities with Chest X-Rays (Poster) | |
| Hip Fracture Risk Modeling Using DXA and Deep Learning (Poster) | |
| StND: Streamline-based Non-rigid partial-Deformation Tractography Registration (Poster) | |
| A Bayesian Unsupervised Deep-Learning Based Approach for Deformable Image Registration (Poster) | |
| Scalable solutions for MR image classification of Alzheimer's disease (Poster) | |