PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.

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Bibliographic Details
Title: PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.
Authors: Chen Z; Center for Machine Learning Research, Peking University, Beijing, China.; Center for Data Science, Peking University, Beijing, China., Nan X; Center for Data Science, Peking University, Beijing, China., Li J; Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China., Zhao J; National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China., Li H; Beijing International Center for Mathematical Research, Peking University, Beijing, China., Lin Z; Center for Data Science, Peking University, Beijing, China., Li H; Center for Data Science, Peking University, Beijing, China., Chen H; Center for Data Science, Peking University, Beijing, China., Liu Y; Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China., Tang L; Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China. tanglei@pku.edu.cn., Zhang L; Center for Data Science, Peking University, Beijing, China. zhangli_pku@pku.edu.cn., Dong B; Center for Machine Learning Research, Peking University, Beijing, China. dongbin@math.pku.edu.cn.; National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China. dongbin@math.pku.edu.cn.; Beijing International Center for Mathematical Research and the New Cornerstone Science Laboratory, Peking University, Beijing, China. dongbin@math.pku.edu.cn.
Source: NPJ digital medicine [NPJ Digit Med] 2025 Dec 02; Vol. 8 (1), pp. 753. Date of Electronic Publication: 2025 Dec 02.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101731738 Publication Model: Electronic Cited Medium: Internet ISSN: 2398-6352 (Electronic) Linking ISSN: 23986352 NLM ISO Abbreviation: NPJ Digit Med Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2398-6352
DOI:10.1038/s41746-025-02087-y