MEDICAL IMAGING MEETS NeurIPS
An official NeurIPS Workshop - 2 December 2022 - In Person (with hybrid provided)
An official NeurIPS Workshop - 2 December 2022 - In Person (with hybrid provided)
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)
Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models
Sajith M Rajapaksa (University of Toronto), et.al
Does Medical Imaging learn different Convolution Filters?
Paul Gavrikov (Offenburg University), et.al
Semi-supervised Learning Using Robust Loss
Wenhui Cui (University of Southern California), et.al
Clinically-guided Prototype Learning and Its Use for Explanation in Alzheimer's Disease Identification
Ahmad Wisnu Mulyadi (Korea University), et.al
Uncertainty in Neural Networks vs. Dermatologists for Skin Lesion Classification
Pieter Van Molle (Ghent University), et.al
Motion-mode Based Prediction of Cardiac Function on Echocardiograms
Thomas M. Sutter (ETH Zurich), et.al
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
Enhancing Annotator Efficiency: Automated Partitioning of a Lung Ultrasound Dataset by View
Bennett VanBerlo (Faculty of Engineering, University of Western Ontario), et.al
Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images
Yi Cui (University of North Carolina at Chapel Hill), et.al
Topological Classification in a Wasserstein Distance Based Vector Space
Tananun Songdechakraiwut (University of Wisconsin-Madison), et.al
Learning SimCLR Representations for Improving Melanoma Whole Slide Images Classification Model Generalization
Yang Jiang (Proscia Inc.), et.al
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
Naga Karthik Enamundram (Mila/Polytechnique Montreal), et.al
Two-stage Conditional Chest X-ray Radiology Report Generation
Pablo Messina (Pontificia Universidad Católica de Chile), et.al
Towards Geometry-Aware Cell Segmentation in Microscopy Images
Zhexu Jin (Duke Kunshan University), et.al
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
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
Subject-specific quantitative susceptibility mapping using patch based deep image priors
Arvind Balachandrasekaran (Harvard Medical School), et.al
Quantifying Explainability of Counterfactual-Guided MRI Feature for Alzheimer's Disease Prediction
Kwanseok Oh (Korea University), et.al
DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging
Zhenghan Fang (Johns Hopkins University), et.al
Label-Free Segmentation of Liver Tumors by Synthesizing Tumor Shape and Texture
Qixin Hu (Huazhong University of Science and Technology), et.al
A Deep Spiking Convolutional Conversion Scheme for Robust Vertebrae Segmentation & Identification
Elon Litman (American Heritage School of Boca Delray)
Learn Complementary Pseudo-label for for Source-free Domain Adaptive Medical Segmentation
Wanqing Xie (AHMU), et.al
Normative Modeling on Multimodal Neuroimaging Data using Variational Autoencoders
Sayantan Kumar (Department of Computer Science and Engineering, Washington University in St. Louis), et.al
UniverSeg: Universal Medical Image Segmentation
Victor I Butoi (MIT), et.al
HyperFed: A Novel Hypernetwork-based Personalized Federated Learning Framework for Multi-source CT Reconstruction
Ziyuan Yang (Sichuan University), et.al
A Framework for Generating 3D Shape Counterfactuals
Rajat R Rasal (Imperial College London), et.al
Assembling Existing Labels from Public Datasets to Diagnose Novel Diseases: COVID-19 in Early 2019
Zengle Zhu (Tongji University), et.al
Labeling Instructions Matter in Biomedical Image Analysis
Tim Rädsch (German Cancer Research Center), et.al
Simulating k-space artifacts for robust CNNs
Yaniel Cabrera (Imperial College London), et.al
The Need for Medically Aware Video Compression in Gastroenterology
Joel Shor (Google), et.al
Deep Learning for Model Correction in Cardiac Electrophysiological Imaging
Victoriya Kashtanova (Inria), et.al
Physically-primed deep-neural-networks for generalized undersampled MRI reconstruction
Nitzan Avidan (Technion - Israel Institute of Technology), et.al
Metrics Reloaded
Annika Reinke (German Cancer Research Center), et.al
Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models
Kumud Lakara (Manipal Institute of Technology), et.al
Structured Priors for Disentangling Pathology and Anatomy in Patient Brain MRI
Anjun Hu (McGill University), et.al
MRI segmentation of the developing neonatal brain
Leonie Richter (Imperial College London), et.al
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
How do 3D image segmentation networks behave across the context versus foreground ratio trade-off?
Amith J Kamath (University of Bern), et.al
Denoising Enhances Visualization of Optical Coherence Tomography Images
Harishwar Reddy K (Indian Institute of Science), et.al
Motion-mode Based Prediction of Cardiac Function on Echocardiograms
Thomas M. Sutter (ETH Zurich), et.al
Unsupervised Anomaly Detection in Medical Images Using Hierarchical Variational Autoencoders
Derek DS Huynh (University of Toronto), et.al
Unsupervised fetal brain MR segmentation using multi-atlas deep learning registration
Valentin Comte (UPF), et.al
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)
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)
Detecting Adversarial Attacks On Breast Cancer Diagnostic Systems Using Attribution-based Confidence Metric
Steven Fernandes (Creighton University), et.al
Effect of Denoising on Retrospective Harmonization of Diffusion Magnetic Resonance Images
Shreyas Fadnavis (Harvard), et.al
Segmentation of Ascites on Abdominal CT Scans for the Assessment of Ovarian Cancer
Benjamin Hou (National Institutes of Health), et.al
Probabilistic Interactive Segmentation for any Medical Image
Hallee E Wong (MIT), et.al
Semi-supervised Learning from Uncurated Echocardiogram Images with Fix-A-Step
Zhe Huang (Tufts University), et.al
Unsupervised feature correlation network for localizing breast cancer using history of mammograms
Jun Bai (University of Connecticut), et.al
Grade-Adjusted Image Analysis Of Breast Cancer To Predict Subtype
DONG NEUCK LEE (UNC-Chapel Hill)
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
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
Learning Probabilistic Topological Representations Using Discrete Morse Theory
Xiaoling Hu (Stony Brook University), et.al
Region-of-Interest Adaptive Acquisition for Accelerated MRI
Zihui Wu (California Institute of Technology), et.al
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
Eloy Geenjaar (Georgia Institute of Technology), et.al
A Radiogenomics-based Coordinate System to Quantify the Heterogeneity of Glioblastoma
Fanyang Yu (University of Pennsylvania), et.al
Semi-Supervision for Clinical Contrast Synthesis from Magnetic Resonance Fingerprinting
Mahmut Yurt (Stanford University), et.al
Transformer Utilization in Medical Image Segmentation Networks
Saikat Roy (German Cancer Research Center (DKFZ)), et.al
Tracking the Dynamics of the Tear Film Lipid Layer
Tejasvi Kothapalli (UC Berkeley)
UniverSeg: Universal Medical Image Segmentation
Victor I Butoi (MIT), et.al
Unpaired Image Translation with Limited Data for Revealing Subtle Phenotypes
Anis Bourou (Université de Paris Descartes), et.al
Improving Instrument Detection for a Robotic Scrub Nurse Using a Multi-Frame Voting Scheme
Jorge A. Badilla-Solórzano (IMES), et.al
StyleReg - Style Transfer as a Preprocess Step for Myocardial T1 Mapping
Eyal Hanania (Technion), et.al
A Hybrid Classifier with Diverse Features Selected from Feature Sets Extracted by Machine Learning Models for Image Classification
Luna M Zhang (Stony Brook University)
Deployment of deep models for intra-operative margin assessment using mass spectrometry
Amoon Jamzad (Queen's University), et.al
Anatomy-informed multimodal learning for myocardial infarction prediction
Ivan-Daniel Sievering (EPFL), et.al
UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection
Pratinav Seth (Manipal Institute of Technology), et.al
Attention-based learning of views fusion applied to myocardial infarction diagnosis from x-ray CT
Jakub Gwizdala (EPFL), et.al
Predicting structural brain trajectories with discrete optimal transport normalizing flows
Mireia Masias (Universitat Pompeu Fabra), et.al
pFLSynth: Personalized Federated Learning of Image Synthesis in Multi-Contrast MRI
Onat Dalmaz (Bilkent University), et.al
Adversarial Diffusion Models for Unsupervised Medical Image Synthesis
Muzaffer Özbey (Bilkent University), et.al
Contrast Invariant Feature Representations for Medical Image Analysis
Yue Zhi, Russ Chua (MIT), et.al
StyleGAN2-based Out-of-Distribution Detection for Medical Imaging
McKell Woodland (Rice University; MD Anderson Cancer Center), et.al
Imbalanced Classification in Medical Imaging via Regrouping
Le Peng (University of Minnesota), et.al
Semi-Supervised Cross-Consistency Contrastive Learning for Nuclei Segmentation in Histology Images
Saad Bashir (University of Warwick), et.al
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
Multiscale Metamorphic VAE for 3D Brain MRI Synthesis
Jaivardhan Kapoor (University of Tuebingen, Germany), et.al
Precise Augmentation and Counting of Helicobacter Pylori in Histology Image
Yufei Cui (McGill University), et.al
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