MEAN
发表于 2025-3-23 10:56:12
http://reply.papertrans.cn/29/2825/282484/282484_11.png
熟练
发表于 2025-3-23 15:51:57
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
prosthesis
发表于 2025-3-23 19:50:25
http://reply.papertrans.cn/29/2825/282484/282484_13.png
人类
发表于 2025-3-24 01:48:59
http://reply.papertrans.cn/29/2825/282484/282484_14.png
套索
发表于 2025-3-24 03:28:43
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
发表于 2025-3-24 09:09:08
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
难听的声音
发表于 2025-3-24 12:49:19
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
违反
发表于 2025-3-24 16:20:36
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
压倒性胜利
发表于 2025-3-24 20:58:20
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
Orgasm
发表于 2025-3-24 23:11:57
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