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

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Conference proceedings 2020st MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic.?..For DART 2020, 12 full papers were accepted from 18

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套索

Augmented Radiology: Patient-Wise Feature Transfer Model for Glioma Grading the purpose of reducing unnecessary biopsies and diagnostic burden, we propose a patient-wise feature transfer model for learning the relationship of phenotypes between radiological images and pathological images. We hypothesize that high-level features from the same patient are possible to be link

Infelicity

Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimagiious 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

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Registration of Histopathology Images Using Self Supervised Fine Grained Feature Mapsration performance. We propose to integrate segmentation information in a registration framework using fine grained feature maps obtained in a self supervised manner. Self supervised feature maps enables use of segmentation information despite the unavailability of manual segmentations. Experimental

違反

Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Spacege 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

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Semi-supervised Pathology Segmentation with Disentangled Representationsvised 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

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Parts2Whole: Self-supervised Contrastive Learning via Reconstructionbanks, making it unappealing for 3D medical imaging, while in 3D medical imaging, reconstruction-based self-supervised learning reaches a new height in performance, but lacks mechanisms to learn contrastive representation; therefore, this paper proposes a new framework for self-supervised contrastiv