Titlebook: Domain Adaptation and Representation Transfer; 4th MICCAI Workshop, Konstantinos Kamnitsas,Lisa Koch,Sotirios Tsaftari Conference proceedin

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Endemic

René Sotelo,Charles F. Polotti,Juan Arriagawo schemes to transfer the gradients information to improve the generalization achieved during pre-training while fine-tuning the model. We show that our methods outperform the . with different levels of data scarcity from the target site, on multiple datasets and tasks.

Peak-Bone-Mass

LAIR

MIRTH

,Supervised Domain Adaptation Using Gradients Transfer for Improved Medical Image Analysis,wo schemes to transfer the gradients information to improve the generalization achieved during pre-training while fine-tuning the model. We show that our methods outperform the . with different levels of data scarcity from the target site, on multiple datasets and tasks.

ILEUM

discord

0302-9743 in conjunction with MICCAI 2022, in September 2022. .DART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/dee

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Fulsome

,Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification,nce disparities between differing skin tones should be addressed before widespread deployment. In this work, we propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images, and use this to annotate the benchmark ISIC dataset. We subsequently use these auto

RACE

,Benchmarking and Boosting Transformers for Medical Image Classification,one representative visual benchmark after another. However, the competition between visual transformers and CNNs in medical imaging is rarely studied, leaving many important questions unanswered. As the first step, we benchmark how well existing transformer variants that use various (supervised and