{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:47:48Z","timestamp":1775137668178,"version":"3.50.1"},"reference-count":32,"publisher":"IOP Publishing","issue":"7","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,4,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Due to the frequent co-occurrence of multiple diseases in patients, automated multi-label diagnosis (MLD) of medical images remains a challenging yet clinically important task, particularly due to two major challenges: (1) feature confusion arises from the loss of disease localization information due to pooling operations, and (2) suboptimal probabilistic modeling capacity and feature expressiveness using vanilla multi-layer perceptrons as the predictors.\n                    <jats:italic>Approach.<\/jats:italic>\n                    In this work, we propose a novel MLD framework that integrates a spatial-disease feature condensation (SDFC) mechanism and Kolmogorov\u2013Arnold layers (KALs). The SDFC module preserves the full spatial resolution of image features by condensing channel-wise information into disease-aligned spatial representations. These spatial-disease features are subsequently processed by a KALs-based classifier, which offers enhanced expressiveness for modeling complex interdependencies among co-occurring diseases.\n                    <jats:italic>Results.<\/jats:italic>\n                    Extensive experiments conducted on two publicly available clinical datasets, ODIR and NIH ChestX-ray, demonstrate that the proposed method consistently outperforms state-of-the-art MLD approaches in terms of diagnostic performance, generalization, and reliability.\n                    <jats:italic>Significance.<\/jats:italic>\n                    By explicitly preserving spatial disease information and leveraging the superior function modeling capability of Kolmogorov\u2013Arnold, the proposed framework provides an effective and robust solution for multi-label medical image diagnosis, with potential value for clinical decision support systems.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae4e7a","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:53:41Z","timestamp":1772837621000},"page":"075007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-label medical diagnosis using spatial-disease feature condensation and Kolmogorov\u2013Arnold layers"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6903-7820","authenticated-orcid":false,"given":"Lang","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7303-1030","authenticated-orcid":true,"given":"Yingyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0275-4098","authenticated-orcid":false,"given":"Ziyuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7201-2092","authenticated-orcid":true,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"pmbae4e7abib1","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","type":"journal-article","article-title":"The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans","volume":"38","author":"Armato III","year":"2011","journal-title":"Med. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-10-15","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-03-06","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-04-02","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}