Titlebook: Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning; Second MICCAI Worksh Shadi Albarqouni,Spyridon B

净礼

GAVEL

Subhadeep Biswas,Ankurita Nath,Anjali Palious methods typically assume that multi-site data are sampled from the same distribution. Such an assumption may not hold in practice due to the data heterogeneity caused by different scanning parameters and subject populations in multiple imaging sites. Even though several deep domain adaptation m

雇佣兵

涂掉

Marc Lunkenheimer,Alexander H. Kracklauerge of images from different modalities has great clinical benefits. However, the generalization ability of deep networks on different modalities is challenging due to domain shift. In this work, we investigate the challenging unsupervised domain adaptation problem of cross-modality medical image seg

热心

Mirna Leko Šimić,Helena Štimac,Sendi Deželićvised approaches by combining labelled and unlabelled data can offer a solution to data scarcity. An approach to semi-supervised learning relies on reconstruction objectives (as self-supervision objectives) that learns in a joint fashion suitable representations for the task. Here, we propose Anatom

Hallmark

智力高

脱离

Sehoon Kwon,Jaechun No,Sung-soon Park sites result in dramatic drops of CNN segmentation quality on unseen domains. Many of the recently proposed MRI domain adaptation methods operate with the last CNN layers to suppress domain shift. At the same time, the core manifestation of MRI variability is a considerable diversity of image inten

充气球

粗糙滥制

G. Gupta,R. Shrivastava,J. Khan,N. K. Singhant against nuisance factors is an open question. This is done by removing sensitive information from the learned representation. Such privacy-preserving representations are believed to be beneficial to some medical and federated learning applications. In this paper, a framework for learning invaria