Medical Image Computing and Computer Assisted Intervention - MICCAI 2021

Attention for Unseen-Domain Generalizable Medical Image Segmentation.- Fully Test-time Adaptation for Image Segmentation.- OLVA: Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation.- Prototypical Interaction Graph for Unsupervised Domain Adaptation in Surgical Instrument Segmentation.- Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation.- Data-driven mapping between functional connectomes using optimal transport.- EndoUDA: A modality independent segmentation approach for endoscopy imaging.- Style Transfer Using Generative Adversarial Networks for Multi-Site MRI Harmonization.- Machine Learning - Federated Learning.- Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching.- FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification.- Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning.- Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures.- Federated Contrastive Learning for Volumetric Medical Image Segmentation.- Federated Contrastive Learning for Decentralized Unlabeled Medical Images.- Machine Learning - Interpretability / Explainability.- Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features.- Demystifying T1-MRI to FDG18-PET Image Translation via Representational Similarity.- Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation.- An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma.- Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data.- SPARTA: An Integrated Stability, Discriminability, and Sparsity based Radiomic Feature Selection Approach.- The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization for Medical Image Segmentation.- Fighting Class Imbalance with ContrastiveLearning.- Interpretable gender classification from retinal fundus images using BagNets.- Explainable Classification of Weakly Annotated Wireless Capsule Endoscopy Images based on a Fuzzy Bag-of-Colour Features Model and Brain Storm Optimization.- Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models.- A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging.- Using Causal Analysis for Conceptual Deep Learning Explanation.- A spherical convolutional neural network for white matter structure imaging via diffusion MRI.- Sharpening Local Interpretable Model-agnostic Explanations for Histopathology: Improved Understandability and Reliability.- Improving the Explainability of Skin Cancer Diagnosis Using CBIR.- PAC Bayesian Performance Guarantees for (Stochastic) Deep Networks in Medical Imaging.- Machine Learning - Uncertainty.- Medical Matting: A New Perspective on Medical Segmentation with Uncertainty.- Confidence-aware Cascaded Network for Fetal Brain Segmentation on MR Images.- Orthogonal Ensemble Networks for Biomedical Image Segmentation.- Learning to Predict Error for MRI Reconstruction.- Uncertainty-Guided Progressive GANs for Medical Image Translation.- Variational Topic Inference for Chest X-Ray Report Generation.- Uncertainty Aware Deep Reinforcement Learning for Anatomical Landmark Detection in Medical Images.