Decomposition based curriculum-style self-training for source-free universal domain adaptation in computational pathology.

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Bibliographic Details
Title: Decomposition based curriculum-style self-training for source-free universal domain adaptation in computational pathology.
Authors: Liu W; School of Management, Hefei University of Technology, Anhui 230009, China; Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui 230009, China. Electronic address: wtliu@mail.hfut.edu.cn., Ni Z; School of Management, Hefei University of Technology, Anhui 230009, China; Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui 230009, China. Electronic address: zhiwein@163.com., Zhu X; School of Management, Hefei University of Technology, Anhui 230009, China; Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology), Anhui 230009, China; Intelligent Decision-making and Information System Technology Engineering Research Center of Ministry of Education, Anhui 230009, China. Electronic address: zhuxuhui@hfut.edu.cn., Chen Q; School of Artificial and Big Data, Hefei University, Anhui 230601, China. Electronic address: chenqian@hfuu.edu.cn., Ni L; School of Management, Hefei University of Technology, Anhui 230009, China; Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Anhui 230009, China. Electronic address: niliping@hfut.edu.cn., Xia P; School of Big Data and Statistics, Anhui University, Hefei 230601, China. Electronic address: xiapingfan@ahu.edu.cn.
Source: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Aug; Vol. 200, pp. 108805. Date of Electronic Publication: 2026 Mar 04.
Publication Type: Comparative Study; Journal Article
Journal Info: Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-2782
DOI:10.1016/j.neunet.2026.108805