Titlebook: Resource-Efficient Medical Image Analysis; First MICCAI Worksho Xinxing Xu,Xiaomeng Li,Huazhu Fu Conference proceedings 2022 The Editor(s)

Affiliation

chance

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-

悶熱

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

,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

Gourmet

,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

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

surmount

,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

啟發(fā)

,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