{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T22:01:04Z","timestamp":1790373664780,"version":"4.1.0"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376130"],"award-info":[{"award-number":["62376130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Weakly supervised pathology segmentation aims to alleviate the reliance on costly pixel-level annotations, yet remains challenging due to severe tissue heterogeneity, complex morphological patterns, and strong contextual confounding in histopathology images. Existing transformer-based methods often learn spurious correlations between lesions and surrounding tissues, while graph-based approaches tend to suffer from feature over-smoothing, resulting in degraded boundary delineation and localization accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To address these challenges, we propose a novel Causal Spectral Segmenter (CSS) for weakly supervised pathology segmentation. The proposed framework seamlessly integrates causal representation learning and multi-scale spectral graph reasoning. Specifically, a Causal Counterfactual Projector (CCP) is introduced to estimate lesion-specific causal effects through factual\u2013counterfactual intervention, thereby suppressing contextual confounding and enhancing lesion-discriminative representations. Furthermore, we develop a Multi-scale Spectral Graph Reasoner (MSGR) composed of stacked Spectral Chebyshev Graph Convolution (SCGC) layers, which perform topology-aware spectral propagation across multiple neighborhood scales to capture long-range tissue dependencies while mitigating graph over-smoothing. By jointly modeling causal effects and multi-scale topological structures, CSS effectively improves lesion localization and boundary preservation under weak supervision. Extensive experiments on two public histopathology segmentation benchmarks demonstrate that CSS consistently outperforms state-of-the-art methods and achieves superior segmentation accuracy and structural consistency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code and implementation details will be publicly available at: https:\/\/github.com\/zhangxu90s\/CSS.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag674","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:49:49Z","timestamp":1788954589000},"source":"Crossref","is-referenced-by-count":0,"title":["Causal spectral segmenter: counterfactual graph reasoning for weakly supervised pathology segmentation"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9294-2841","authenticated-orcid":false,"given":"Xu","family":"Zhang","sequence":"first","affiliation":[{"name":"Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences , Jinan,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiasheng","family":"Si","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences) , Jinan,","place":["China"]},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science , Jinan,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenpeng","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences) , Jinan,","place":["China"]},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science , Jinan,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Li","sequence":"additional","affiliation":[{"name":"Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences , Jinan,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences , Jinan,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"2026092517312043700_btag674-B1","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat Med"},{"key":"2026092517312043700_btag674-B2","author":"Chan","year":"2019"},{"key":"2026092517312043700_btag674-B3","doi-asserted-by":"publisher","first-page":"102482","DOI":"10.1016\/j.media.2022.102482","article-title":"Weakly supervised 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