Titlebook: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse; Third MICCAI Worksho Shadi Albarqouni

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A Systematic Benchmarking Analysis of?Transfer Learning for Medical Image?Analysisd to benchmark the efficacy of newly-developed pre-training techniques for medical image analysis, leaving several important questions unanswered. As the first step in this direction, we conduct a systematic study on the transferability of models pre-trained on iNat2021, the most recent large-scale

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FDA: Feature Decomposition and?Aggregation for Robust Airway Segmentationset while the public airway datasets are mainly clean CT scans with coarse annotation, thus difficult to be generalized to noisy CT scans (e.g. COVID-19 CT scans). In this work, we proposed a new dual-stream network to address the variability between the clean domain and noisy domain, which utilizes

lattice

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Self-supervised Learning of Inter-label Geometric Relationships for Gleason Grade Segmentationased segmentation methods achieve state-of-the-art accuracy, they rely on large datasets with manual annotations. We propose a method to synthesize PCa histopathology images by learning the geometrical relationship between different disease labels using self-supervised learning. Manual segmentation

Wernickes-area

Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training of many computer vision methods, including those developed for medical image segmentation. These methods jointly train a segmentor and an adversarial mask discriminator, which provides a data-driven shape prior. At inference, the discriminator is discarded, and only the segmentor is used to predict

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Transductive Image Segmentation: Self-training and Effect of Uncertainty Estimationsed mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracti

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Unsupervised Domain Adaptation with Semantic Consistency Across Heterogeneous Modalities for MRI Progeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel spac