Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration-Reference-Cited by-同舟云学术

Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

Published:2023-12 Issue: Volume:90 Page:102935
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Li Yiwen^ORCID,Fu Yunguan^ORCID,Gayo Iani J.M.B.^ORCID,Yang Qianye^ORCID,Min Zhe,Saeed Shaheer U.^ORCID,Yan Wen^ORCID,Wang Yipei^ORCID,Noble J. Alison^ORCID,Emberton Mark^ORCID,Clarkson Matthew J.^ORCID,Huisman Henkjan^ORCID,Barratt Dean C.^ORCID,Prisacariu Victor A.^ORCID,Hu Yipeng^ORCID

Funder

Wellcome Trust

University of Manchester

Knight Cancer Institute, Oregon Health and Science University

CRUK

Engineering and Physical Sciences Research Council

Cancer Research UK

University College London

University of Cambridge

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference55 articles.

1. FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection;Abdel-Basset;Knowl.-Based Syst.,2021

2. Data from prostate-diagnosis;Bloch;Cancer Imaging Arch.,2015

3. MONAI: An open-source framework for deep learning in healthcare;Cardoso,2022

4. Data from prostate-MRI;Choyke;Cancer Imaging Arch.,2016

5. A unified framework for generalized low-shot medical image segmentation with scarce data;Cui;IEEE Trans. Med. Imaging,2020

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Query-guided generalizable medical image segmentation;Pattern Recognition Letters;2024-08

2. Few-Shot Medical Image Segmentation via Generating Multiple Representative Descriptors;IEEE Transactions on Medical Imaging;2024-06

3. A survey of emerging applications of diffusion probabilistic models in MRI;Meta-Radiology;2024-06

4. Attentional adversarial training for few-shot medical image segmentation without annotations;PLOS ONE;2024-05-02

5. A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation;NMR in Biomedicine;2024-03-24