{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:12:32Z","timestamp":1785427952785,"version":"3.56.0"},"posted":{"date-parts":[[2025,5,22]]},"group-title":"Bioinformatics","reference-count":39,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Boston University Kilachand Fund"},{"award":["P30AG13846"],"award-info":[{"award-number":["P30AG13846"]}],"id":[{"id":"https:\/\/ror.org\/049v75w11","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,5,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Chronic Traumatic Encephalopathy (CTE) and other neurodegenerative disorders (NDs) pose diagnostic challenges due to their diffuse and subtle pathological changes. Traditional diagnostic methods relying on manual histopathological slide inspection are labor-intensive and prone to variability, often missing subtle structural alterations. This study introduces an age-informed, attention-based multiple instance learning (MIL) pipeline to predict AT8 density, a key marker of p-tau aggregation in CTE. Using Luxol Fast Blue and Hematoxylin &amp; Eosin (LH&amp;E) stained images, our model identifies critical pathological regions and generates interpretable attention maps highlighting structural changes linked to tau pathology. Incorporating patient age enhances predictive accuracy and contextual understanding, addressing aging\u2019s confounding effects. We also develop quantitative evaluation procedures for foundation models (FMs), assessing attention map smoothness, faithfulness, and robustness to perturbations like stain variability and noise. These benchmarks facilitate informed FM selection and optimization for neuropathological tasks. By enabling scalable, automated whole-slide image (WSI) analysis, our approach advances digital neuropathology, supporting earlier and more precise ND diagnoses and uncovering subtle markers with potential applications in clinical imaging.<\/jats:p>","DOI":"10.1101\/2025.05.16.654510","type":"posted-content","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T10:30:18Z","timestamp":1748082618000},"source":"Crossref","is-referenced-by-count":0,"title":["Age-informed, attention-based weakly supervised learning for neuropathological image assessment"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3253-6304","authenticated-orcid":false,"given":"Shuying","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxwell","family":"Malamut","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ann","family":"McKee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1257-981X","authenticated-orcid":false,"given":"Jonathan D.","family":"Cherry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1316-4456","authenticated-orcid":false,"given":"Lei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2025060122113022000_2025.05.16.654510v1.1","doi-asserted-by":"publisher","DOI":"10.1007\/s11940-014-0306-5"},{"key":"2025060122113022000_2025.05.16.654510v1.2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/4935915"},{"key":"2025060122113022000_2025.05.16.654510v1.3","doi-asserted-by":"publisher","DOI":"10.1007\/s00401-015-1515-z"},{"key":"2025060122113022000_2025.05.16.654510v1.4","doi-asserted-by":"publisher","DOI":"10.1093\/jnen\/nlab001"},{"key":"2025060122113022000_2025.05.16.654510v1.5","doi-asserted-by":"publisher","DOI":"10.1093\/jnen\/nlac065"},{"key":"2025060122113022000_2025.05.16.654510v1.6","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2023.2907"},{"key":"2025060122113022000_2025.05.16.654510v1.7","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03141-0"},{"key":"2025060122113022000_2025.05.16.654510v1.8","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02856-4"},{"key":"2025060122113022000_2025.05.16.654510v1.9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102559"},{"key":"2025060122113022000_2025.05.16.654510v1.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07441-w"},{"key":"2025060122113022000_2025.05.16.654510v1.11","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02857-3"},{"key":"2025060122113022000_2025.05.16.654510v1.12","unstructured":"Zimmermann E , Vorontsov E , Viret J , et al (2024) Virchow 2: Scaling Self-Supervised Mixed Magnification Models in Pathology. 1\u201319"},{"key":"2025060122113022000_2025.05.16.654510v1.13","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02856-4"},{"key":"2025060122113022000_2025.05.16.654510v1.14","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02504-3"},{"key":"2025060122113022000_2025.05.16.654510v1.15","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03141-0"},{"key":"2025060122113022000_2025.05.16.654510v1.16","first-page":"3376","article-title":"Attention-based deep multiple instance learning","volume":"5","year":"2018","journal-title":"35th International Conference on Machine Learning, ICML 2018"},{"key":"2025060122113022000_2025.05.16.654510v1.17","unstructured":"Shao Z , Bian H , Chen Y , et al (2021) TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification. 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