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Workshop: Medical Imaging Meets NeurIPS

Jonas Teuwen, Marleen de Bruijne, Qi Dou, Ben Glocker, Ipek Oguz, Aasa Feragen, Hervé Lombaert, Ender Konukoglu

2020-12-12T02:30:00-08:00 - 2020-12-12T11:25:00-08:00
Abstract: '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.


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2020-12-12T02:30:00-08:00 - 2020-12-12T03:00:00-08:00
Keynote by Lena Maier-Hein: Addressing the Data Bottleneck in Biomedical Image Analysis
Lena Maier-Hein
2020-12-12T03:10:00-08:00 - 2020-12-12T03:20:00-08:00
DeepSim: Semantic similarity metrics for learned image registration
Steffen Czolbe
2020-12-12T03:20:00-08:00 - 2020-12-12T03:30:00-08:00
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks
Darya Trofimova
2020-12-12T03:30:00-08:00 - 2020-12-12T05:00:00-08:00
Poster Session 1
2020-12-12T05:00:00-08:00 - 2020-12-12T05:30:00-08:00
Keynote by Nathan Silberman: Real-world Insights from Patient-facing Machine Learning Models
Nathan Silberman
2020-12-12T05:40:00-08:00 - 2020-12-12T05:50:00-08:00
Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images
Kathryn Schutte
2020-12-12T05:50:00-08:00 - 2020-12-12T06:00:00-08:00
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
Li Sun
2020-12-12T06:00:00-08:00 - 2020-12-12T06:45:00-08:00
2020-12-12T06:45:00-08:00 - 2020-12-12T07:15:00-08:00
Keynote by Spyridon Bakas: The Federated Tumor Segmentation (FeTS) Initiative: Towards a paradigm-shift in multi-institutional collaborations
Spyros Bakas
2020-12-12T07:25:00-08:00 - 2020-12-12T07:35:00-08:00
Deep learning to assist radiologists in breast cancer diagnosis with ultrasound imaging
Yiqiu Shen
2020-12-12T07:35:00-08:00 - 2020-12-12T07:45:00-08:00
Privacy-preserving medical image analysis
Alex Ziller
2020-12-12T07:45:00-08:00 - 2020-12-12T09:00:00-08:00
Poster Session 2
2020-12-12T09:00:00-08:00 - 2020-12-12T09:30:00-08:00
Keynote by Jerry Prince: New Approaches for Magnetic Resonance Image Harmonization
Jerry L Prince
2020-12-12T09:40:00-08:00 - 2020-12-12T09:50:00-08:00
Brain2Word: Improving Brain Decoding Methods and Evaluation
Damian Pascual Ortiz
2020-12-12T09:50:00-08:00 - 2020-12-12T10:00:00-08:00
3D Infant Pose Estimation Using Transfer Learning
Simon Ellershaw
2020-12-12T10:00:00-08:00 - 2020-12-12T10:10:00-08:00
FastMRI Introduction
Matthew J Muckley
2020-12-12T10:10:00-08:00 - 2020-12-12T10:15:00-08:00
Q&A FastMRI introduction
2020-12-12T10:15:00-08:00 - 2020-12-12T10:25:00-08:00
FastMRI Talk 1
Mahmoud Mostapha
2020-12-12T10:25:00-08:00 - 2020-12-12T10:35:00-08:00
FastMRI Talk 2
Zaccharie Ramzi
2020-12-12T10:35:00-08:00 - 2020-12-12T10:45:00-08:00
FastMRI Talk 3
Sunwoo Kim
2020-12-12T10:45:00-08:00 - 2020-12-12T10:50:00-08:00
Q&A FastMRI talk 1-3
2020-12-12T10:50:00-08:00 - 2020-12-12T11:20:00-08:00
FastMRI keynote
Yvonne Lui
Clinical Validation of Machine Learning Algorithm Generated Images
Fred Kwon
Joint Hierarchical Bayesian Learning of Full-structure Noise for Brain Source Imaging
Ali Hashemi
Quantification of task similarity for efficient knowledge transfer in biomedical image analysis
Patrick Scholz
A Bayesian Unsupervised Deep-Learning Based Approach for Deformable Image Registration
Samah Khawaled
Embracing the Disharmony in Heterogeneous Medical Data
Rongguang Wang
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN
Li Sun
Semantic Video Segmentation for Intracytoplasmic Sperm Injection Procedures
Peter He
Comparing Sparse and Deep Neural Network(NN)s: Using AI to Detect Cancer.
Charles Strauss
Ultrasound Diagnosis of COVID-19: Robustness and Explainability
Jay Roberts
Decoding Brain States: Clustering fMRI Dynamic Functional Connectivity Timeseries with Deep Autoencoders
Arthur Spencer
Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI
Anindo Saha
LVHNet: Detecting Cardiac Structural Abnormalities with Chest X-Rays
Shreyas Bhave
Can We Learn to Explain Chest X-Rays?: A Cardiomegaly Use Case
Neil Jethani
StND: Streamline-based Non-rigid partial-Deformation Tractography Registration
Bramsh Q Chandio
Unsupervised detection of Hypoplastic Left Heart Syndrome in fetal screening
Elisa Chotzoglou
COVIDNet-S: SARS-CoV-2 lung disease severity grading of chest X-rays using deep convolutional neural networks
Alexander Wong
AI system for predicting the deterioration of COVID-19 patients in the emergency department
Farah Shamout
Annotation-Efficient Deep Semi-Supervised Learning for Automatic Knee Osteoarthritis Severity Diagnosis from Plain Radiographs
Hoang Nguyen
A Deep Learning Model to Detect Anemia from Echocardiography
Weston Hughes
Hip Fracture Risk Modeling Using DXA and Deep Learning
Peter Sadowski
Classification with a domain shift in medical imaging
Alessandro Fontanella
3D UNet with GAN discriminator for robust localisation of the fetal brain and trunk in MRI with partial coverage of the fetal body
Alena Uus
Biomechanical modelling of brain atrophy through deep learning
Mariana da Silva
Deep Learning extracts novel MRI biomarkers for Alzheimer’s disease progression
Yi Li
Towards disease-aware image editing of chest X-rays
Aakash Saboo
Learning MRI contrast agnostic registration
Malte Hoffmann, Adrian Dalca
A Critic Evaluation Of Covid-19 Automatic Detection From X-Ray Images
Gianluca Maguolo
RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting
Benjamin Hou
Autoencoder Image Compression Algorithm for Reduction of Resource Requirements
Fred Kwon
Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation
Akshay Iyer
Multi-Label Incremental Few-Shot Learning for Medical Image Pathology classifiers
Laleh Seyyed-Kalantari
Diffusion MRI-based structural connectivity robustly predicts "brain-age''
Guruprasath Gurusamy
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
Sam Motamed
Harmonization and the Worst Scanner Syndrome
Daniel Moyer
MVD-Fuse: Detection of White Matter Degeneration via Multi-View Learning of Diffusion Microstructure
Shreyas Fadnavis
Zero-dose PET Reconstruction with Missing Input by U-Net with Attention Modules
Jiahong Ouyang
Predicting the Need for Intensive Care for COVID-19 Patients using Deep Learning on Chest Radiography
Isabelle Hu
Community Detection in Medical Image Datasets: Using Wavelets and Spectral Clustering
Roozbeh Yousefzadeh
Semi-Supervised Learning of MR Image Synthesis without Fully-Sampled Ground-Truth Acquisitions
Mahmut Yurt
Probabilistic Recovery of Missing Phase Images in Contrast-Enhanced CT
Dhruv Patel
Scalable solutions for MR image classification of Alzheimer's disease
Sarah Brueningk
Attention Transfer Outperforms Transfer Learning in Medical Image Disease Classifiers
Sina Akbarian
Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning
Soumick Chatterjee
Modified VGG16 Network for Medical Image Analysis
Amulya Vatsavai
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation
Khrystyna Faryna
Self-supervised out-of-distribution detection in brain CT scans
Seong Tae Kim
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
Andrei Margeloiu