Affiliation
发表于 2025-3-25 05:32:34
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chance
发表于 2025-3-25 08:26:24
0302-9743 in conjunction with MICCAI 2022, in September 2022 as a hybrid event. ..REMIA 2022 accepted 13 papers from the 19 submissions received. The workshop aims at creating a discussion on the issues for practical applications of medical imaging systems with data, label and hardware limitations..978-3-031-
闷热
发表于 2025-3-25 13:28:55
0302-9743 ims at creating a discussion on the issues for practical applications of medical imaging systems with data, label and hardware limitations..978-3-031-16875-8978-3-031-16876-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
Omnipotent
发表于 2025-3-25 18:19:08
,An Efficient Defending Mechanism Against Image Attacking on Medical Image Segmentation Models, models from attacks. Our result on several medical well-known benchmark datasets shows that the proposed defending mechanism to enhance the segmentation models is effective with high scores and better compared to other strong methods.
Fatten
发表于 2025-3-25 23:32:48
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Gourmet
发表于 2025-3-26 03:04:11
,Multi-task Semi-supervised Learning for Vascular Network Segmentation and Renal Cell Carcinoma Clasentation from Hematoxylin and Eosin (H &E) staining histopathological images is still a challenge due to the background complexity. Moreover, there is a lack of large manually annotated vascular network databases. In this paper, we propose a method that reduces reliance on labeled data through semi-
天真
发表于 2025-3-26 05:49:33
Self-supervised Antigen Detection Artificial Intelligence (SANDI),cell phenotyping in multiplex images require extensive annotation workload due to the need for fully supervised training. To overcome this challenge, we develop SANDI, a self-supervised-based pipeline that learns intrinsic similarities in unlabeled cell images to mitigate the requirement for expert
optic-nerve
发表于 2025-3-26 09:43:20
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surmount
发表于 2025-3-26 13:10:20
,Single Domain Generalization via Spontaneous Amplitude Spectrum Diversification, realistic-yet-challenging scenario as a new research line, termed single domain generalization (single-DG), which aims to generalize a model trained on single source domain to multiple target domains. The existing single-DG approaches tried to address the problem by generating diverse samples using
启发
发表于 2025-3-26 19:23:28
,Triple-View Feature Learning for Medical Image Segmentation,h labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation framework. It uses . feature learning on a limited amount of labelled data and a large amount of unlabeled data. The triple-view architecture consists of three pixel-level classifiers and a low-level shared-weight learni