Toward Source-Free Cross Tissues Histopathological Cell Segmentation via Target-Specific Finetuning-Reference-Cited by-同舟云学术

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Toward Source-Free Cross Tissues Histopathological Cell Segmentation via Target-Specific Finetuning

Published:2023-09 Issue:9 Volume:42 Page:2666-2677
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Li Zhongyu¹^ORCID,Li Chaoqun¹,Luo Xiangde²,Zhou Yitian¹,Zhu Jihua¹^ORCID,Xu Cunbao³,Yang Meng⁴,Wu Yenan⁴,Chen Yifeng³^ORCID

Affiliation:

1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

2. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China

3. Department of Pathology, Quanzhou First Hospital affiliated to Fujian Medical University, Quanzhou, China

4. Frontline Intelligent Technology (Nanjing) Company Ltd., Nanjing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10236920/10087318.pdf?arnumber=10087318

Reference75 articles.

1. Trends in the US and Canadian Pathologist Workforces From 2007 to 2017

2. Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

3. A fuzzy K-nearest neighbor algorithm

4. DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

5. Adversarial domain adaptation for cell segmentation;Haq

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1. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy;Light: Science & Applications;2024-09-14

2. A semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning;Frontiers in Oncology;2024-06-19

3. Self-Mining the Confident Prototypes for Source-Free Unsupervised Domain Adaptation in Image Segmentation;IEEE Transactions on Multimedia;2024

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