MEDICAL IMAGING MEETS NeurIPS

An official NeurIPS Workshop - 2 December 2022 - In Person (with hybrid provided)

SCHEDULE

Friday, 2 Dec 2022, 08:55 AM - 05:20 PM (New Orleans Time)

Session I, 08:55 AM - 10:30 AM (Moderator: Qi Dou)

08:55 AM Welcome & Opening Remarks

09:00 AM Keynote: Democratizing surgical skills - video analysis for quantifying surgical expertise – Stefanie Speidel (NCT Dresden, Germany) - Virtual

09:40 AM Keynote: FUTURE-AI: Recommendations for trustworthy AI in medical imaging – Karim Lekadir (University of Barcelona) - Virtual

10:20 AM Oral: CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs – Eloy Geenjaar (Georgia Institute of Technology)

10:30 AM - 11:10 AM, Poster Session & Coffee Break, Gather.Town (for virtual posters)

In-person Posters I

  1. Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models

Sajith M Rajapaksa (University of Toronto), et.al

  1. Does Medical Imaging learn different Convolution Filters?

Paul Gavrikov (Offenburg University), et.al

  1. Semi-supervised Learning Using Robust Loss

Wenhui Cui (University of Southern California), et.al

  1. Clinically-guided Prototype Learning and Its Use for Explanation in Alzheimer's Disease Identification

Ahmad Wisnu Mulyadi (Korea University), et.al

  1. Uncertainty in Neural Networks vs. Dermatologists for Skin Lesion Classification

Pieter Van Molle (Ghent University), et.al

  1. Motion-mode Based Prediction of Cardiac Function on Echocardiograms

Thomas M. Sutter (ETH Zurich), et.al

  1. Automatic Identification of the Lung Sliding artefact on Lung Ultrasound Examination

Blake VanBerlo (David R. Cheriton School of Computer Science, University of Waterloo), et.al

  1. Enhancing Annotator Efficiency: Automated Partitioning of a Lung Ultrasound Dataset by View

Bennett VanBerlo (Faculty of Engineering, University of Western Ontario), et.al

  1. Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images

Yi Cui (University of North Carolina at Chapel Hill), et.al

  1. Topological Classification in a Wasserstein Distance Based Vector Space

Tananun Songdechakraiwut (University of Wisconsin-Madison), et.al

  1. Learning SimCLR Representations for Improving Melanoma Whole Slide Images Classification Model Generalization

Yang Jiang (Proscia Inc.), et.al

  1. Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?

Naga Karthik Enamundram (Mila/Polytechnique Montreal), et.al

  1. Two-stage Conditional Chest X-ray Radiology Report Generation

Pablo Messina (Pontificia Universidad Católica de Chile), et.al

  1. Towards Geometry-Aware Cell Segmentation in Microscopy Images

Zhexu Jin (Duke Kunshan University), et.al

  1. Breast Cancer Pathologic Complete Response Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging

Chi-en A Tai (University of Waterloo), et.al

  1. COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT Images

Hayden Gunraj (University of Waterloo), et.al

  1. Subject-specific quantitative susceptibility mapping using patch based deep image priors

Arvind Balachandrasekaran (Harvard Medical School), et.al

  1. Quantifying Explainability of Counterfactual-Guided MRI Feature for Alzheimer's Disease Prediction

Kwanseok Oh (Korea University), et.al

  1. DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging

Zhenghan Fang (Johns Hopkins University), et.al

Virtual Posters I: Gather.Town

  1. Label-Free Segmentation of Liver Tumors by Synthesizing Tumor Shape and Texture

Qixin Hu (Huazhong University of Science and Technology), et.al

  1. A Deep Spiking Convolutional Conversion Scheme for Robust Vertebrae Segmentation & Identification

Elon Litman (American Heritage School of Boca Delray)

  1. Learn Complementary Pseudo-label for for Source-free Domain Adaptive Medical Segmentation

Wanqing Xie (AHMU), et.al

  1. Normative Modeling on Multimodal Neuroimaging Data using Variational Autoencoders

Sayantan Kumar (Department of Computer Science and Engineering, Washington University in St. Louis), et.al

  1. UniverSeg: Universal Medical Image Segmentation

Victor I Butoi (MIT), et.al

  1. HyperFed: A Novel Hypernetwork-based Personalized Federated Learning Framework for Multi-source CT Reconstruction

Ziyuan Yang (Sichuan University), et.al

  1. A Framework for Generating 3D Shape Counterfactuals

Rajat R Rasal (Imperial College London), et.al

  1. Assembling Existing Labels from Public Datasets to Diagnose Novel Diseases: COVID-19 in Early 2019

Zengle Zhu (Tongji University), et.al

  1. Labeling Instructions Matter in Biomedical Image Analysis

Tim Rädsch (German Cancer Research Center), et.al

  1. Simulating k-space artifacts for robust CNNs

Yaniel Cabrera (Imperial College London), et.al

  1. The Need for Medically Aware Video Compression in Gastroenterology

Joel Shor (Google), et.al

  1. Deep Learning for Model Correction in Cardiac Electrophysiological Imaging

Victoriya Kashtanova (Inria), et.al

  1. Physically-primed deep-neural-networks for generalized undersampled MRI reconstruction

Nitzan Avidan (Technion - Israel Institute of Technology), et.al

  1. Metrics Reloaded

Annika Reinke (German Cancer Research Center), et.al

  1. Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models

Kumud Lakara (Manipal Institute of Technology), et.al

  1. Structured Priors for Disentangling Pathology and Anatomy in Patient Brain MRI

Anjun Hu (McGill University), et.al

  1. MRI segmentation of the developing neonatal brain

Leonie Richter (Imperial College London), et.al

  1. Detecting COVID-19 infection from ultrasound imaging with only five shots: A high-performing explainable deep few-shot learning network

Jessy Song (University of Waterloo), et.al

  1. How do 3D image segmentation networks behave across the context versus foreground ratio trade-off?

Amith J Kamath (University of Bern), et.al

  1. Denoising Enhances Visualization of Optical Coherence Tomography Images

Harishwar Reddy K (Indian Institute of Science), et.al

  1. Motion-mode Based Prediction of Cardiac Function on Echocardiograms

Thomas M. Sutter (ETH Zurich), et.al

  1. Unsupervised Anomaly Detection in Medical Images Using Hierarchical Variational Autoencoders

Derek DS Huynh (University of Toronto), et.al

  1. Unsupervised fetal brain MR segmentation using multi-atlas deep learning registration

Valentin Comte (UPF), et.al

Session II, 11:10 AM - 12:40 PM (Moderator: Danielle Pace)

11:10 AM Keynote: Designing for impact AI-enabled point-of-care imaging – Purang Abolmaesumi (The University of British Columbia) - On-Site

11:50 AM Keynote: Neuroimage analysis in autism: from model-based estimation to data-driven learning – James Duncan (Yale University) - Virtual

12:30 PM Oral: Subject-specific quantitative susceptibility mapping using patch based deep image priors – Arvind Balachandrasekaran (Harvard Medical School)

12:40 AM - 01:40 PM, Lunch Break

Session III, 01:40 PM - 03:20 PM (Moderator: Xiaoxiao Li)

01:40 PM Keynote: Facing the global health challenges in population health and oncology via scalable AI tools – Le Lu (Alibaba Damo Academy) - Virtual

02:20 PM Keynote: A tale of two frontiers: When brain meets AI – Ruogu Fang (University of Florida) - On-Site

03:00 PM Orals:

  • 03:00 PM - pFLSynth: Personalized federated learning of image synthesis in multi-contrast MRI – Onat Dalmaz (Bilkent University)

  • 03:10 PM - Clinically-guided prototype learning and its use for explanation in Alzheimer's disease identification – Ahmad Wisnu Mulyadi (Korea University)

03:20 PM - 04:20 PM, Poster Session & Coffee Break, Gather.Town (for virtual posters)

In-person Posters II

  1. Detecting Adversarial Attacks On Breast Cancer Diagnostic Systems Using Attribution-based Confidence Metric

Steven Fernandes (Creighton University), et.al

  1. Effect of Denoising on Retrospective Harmonization of Diffusion Magnetic Resonance Images

Shreyas Fadnavis (Harvard), et.al

  1. Segmentation of Ascites on Abdominal CT Scans for the Assessment of Ovarian Cancer

Benjamin Hou (National Institutes of Health), et.al

  1. Probabilistic Interactive Segmentation for any Medical Image

Hallee E Wong (MIT), et.al

  1. Semi-supervised Learning from Uncurated Echocardiogram Images with Fix-A-Step

Zhe Huang (Tufts University), et.al

  1. Unsupervised feature correlation network for localizing breast cancer using history of mammograms

Jun Bai (University of Connecticut), et.al

  1. Grade-Adjusted Image Analysis Of Breast Cancer To Predict Subtype

DONG NEUCK LEE (UNC-Chapel Hill)

  1. COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics

Maya Pavlova (University of Waterloo), et.al

  1. Exploring the Relationship Between Model Prediction Uncertainty and Gradient Inversion Attack Vulnerability for Federated Learning-Based Diabetic Retinopathy Grade Classification

Christopher Nielsen (University of Calgary), et.al

  1. Learning Probabilistic Topological Representations Using Discrete Morse Theory

Xiaoling Hu (Stony Brook University), et.al

  1. Region-of-Interest Adaptive Acquisition for Accelerated MRI

Zihui Wu (California Institute of Technology), et.al

  1. CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs

Eloy Geenjaar (Georgia Institute of Technology), et.al

  1. A Radiogenomics-based Coordinate System to Quantify the Heterogeneity of Glioblastoma

Fanyang Yu (University of Pennsylvania), et.al

  1. Semi-Supervision for Clinical Contrast Synthesis from Magnetic Resonance Fingerprinting

Mahmut Yurt (Stanford University), et.al

  1. Transformer Utilization in Medical Image Segmentation Networks

Saikat Roy (German Cancer Research Center (DKFZ)), et.al

  1. Tracking the Dynamics of the Tear Film Lipid Layer

Tejasvi Kothapalli (UC Berkeley)

  1. UniverSeg: Universal Medical Image Segmentation

Victor I Butoi (MIT), et.al

  1. Unpaired Image Translation with Limited Data for Revealing Subtle Phenotypes

Anis Bourou (Université de Paris Descartes), et.al

Virtual Posters II: Gather.Town

  1. Improving Instrument Detection for a Robotic Scrub Nurse Using a Multi-Frame Voting Scheme

Jorge A. Badilla-Solórzano (IMES), et.al

  1. StyleReg - Style Transfer as a Preprocess Step for Myocardial T1 Mapping

Eyal Hanania (Technion), et.al

  1. A Hybrid Classifier with Diverse Features Selected from Feature Sets Extracted by Machine Learning Models for Image Classification

Luna M Zhang (Stony Brook University)

  1. Deployment of deep models for intra-operative margin assessment using mass spectrometry

Amoon Jamzad (Queen's University), et.al

  1. Anatomy-informed multimodal learning for myocardial infarction prediction

Ivan-Daniel Sievering (EPFL), et.al

  1. UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection

Pratinav Seth (Manipal Institute of Technology), et.al

  1. Attention-based learning of views fusion applied to myocardial infarction diagnosis from x-ray CT

Jakub Gwizdala (EPFL), et.al

  1. Predicting structural brain trajectories with discrete optimal transport normalizing flows

Mireia Masias (Universitat Pompeu Fabra), et.al

  1. pFLSynth: Personalized Federated Learning of Image Synthesis in Multi-Contrast MRI

Onat Dalmaz (Bilkent University), et.al

  1. Adversarial Diffusion Models for Unsupervised Medical Image Synthesis

Muzaffer Özbey (Bilkent University), et.al

  1. Contrast Invariant Feature Representations for Medical Image Analysis

Yue Zhi, Russ Chua (MIT), et.al

  1. StyleGAN2-based Out-of-Distribution Detection for Medical Imaging

McKell Woodland (Rice University; MD Anderson Cancer Center), et.al

  1. Imbalanced Classification in Medical Imaging via Regrouping

Le Peng (University of Minnesota), et.al

  1. Semi-Supervised Cross-Consistency Contrastive Learning for Nuclei Segmentation in Histology Images

Saad Bashir (University of Warwick), et.al

  1. From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning

Vishwa S Parekh (University of Maryland School of Medicine), et.al

  1. Multiscale Metamorphic VAE for 3D Brain MRI Synthesis

Jaivardhan Kapoor (University of Tuebingen, Germany), et.al

  1. Precise Augmentation and Counting of Helicobacter Pylori in Histology Image

Yufei Cui (McGill University), et.al

Session IV, 04:20 PM - 5:20PM (Moderator: Yuankai Huo)

04:20 PM Orals:

  • 04:20 PM - Learning probabilistic topological representations using discrete morse theory – Xiaoling Hu (Stony Brook University)

  • 04:30 PM - Exploring the relationship between model prediction uncertainty and gradient inversion attack vulnerability for federated learning-based diabetic retinopathy grade classification – Christopher Nielsen (University of Calgary)

04:40 PM Keynote: What is wrong with medical AI? – Lauren Oakden-Rayner (University of Adelaide) - Virtual

05:20 PM Closing Remarks