{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:44:24Z","timestamp":1792266264077,"version":"4.4.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Indianan University internal","award":["44-875-58"],"award-info":[{"award-number":["44-875-58"]}]},{"name":"Melvin and Bren Simon Comprehensive Cancer Center","id":[{"id":"https:\/\/ror.org\/00g1d7b60","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Accurate detection and classification of individual cells are essential for quantitative histopathology. In Ki-67-based tumor grading, the clinically relevant index is calculated from immunopositive and immunonegative tumor cells, while non-tumor cells should be excluded. This is challenging because Ki-67-stained tumor regions often contain a heterogeneous mixture of immunopositive tumor cells, immunonegative tumor cells, and non-tumor cells. Here, we study cell detection and classification in Ki-67-stained histopathology images to support grading of pancreatic neuroendocrine tumors (PanNETs), where reliable tumor-cell-specific identification is critical for tumor grading and decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present ShadoNet, a U-Net\u2013style encoder\u2013decoder framework that integrates shape priors with structured regression for cell detection and classification. ShadoNet predicts class-specific proximity maps that emphasize cell centers and jointly encode spatial location, morphology, and class identity through smooth intensity decay from cell centers. Across multiple Ki-67-stained histopathology datasets, incorporating shape information improved performance on standard detection and classification metrics, indicating that shape-derived priors provide useful class-related cues\u2014without requiring full instance-level boundary annotations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>ShadoNet uses the Segment Anything Model (SAM) to generate initial cellular shape masks, which are refined using human-annotated nuclear point labels to construct ground-truth proximity maps. During training, shape guidance is enforced via two auxiliary objectives: (i) a rotation-extended Scaled IoU (SIoU) loss to capture orientation and shape alignment, and (ii) a Hausdorff Distance Transform (HDT) loss to penalize boundary discrepancies between predictions and ground truth. Code is publicly available at: https:\/\/github.com\/GhasemiGOF\/ShadoNet. A fixed version of the code used for the experiments in this manuscript has been archived at Zenodo with DOI: https:\/\/doi.org\/10.5281\/zenodo.21050210. BCData and SHIDC-B-Ki-67 are publicly available datasets and can be accessed through their original publications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag704","type":"journal-article","created":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T11:32:02Z","timestamp":1790508722000},"source":"Crossref","is-referenced-by-count":0,"title":["ShadoNet: a cell detection and classification framework for Ki-67 pathology images"],"prefix":"10.1093","volume":"42","author":[{"given":"Mahsa","family":"Ghasemi","sequence":"first","affiliation":[{"name":"Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington Department of Computer Science, , Bloomington, IN 47405,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyong","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado 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