{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:01:15Z","timestamp":1792785675614,"version":"4.5.0"},"reference-count":27,"publisher":"IOP Publishing","issue":"16","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01 CA260426"],"award-info":[{"award-number":["R01 CA260426"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000738","name":"U.S. Department of Veterans Affairs","doi-asserted-by":"crossref","award":["BLR&D I01 BX005598"],"award-info":[{"award-number":["BLR&D I01 BX005598"]}],"id":[{"id":"10.13039\/100000738","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Shandong Province for Distinguished Young Scholars","award":["2023HWYQ-027"],"award-info":[{"award-number":["2023HWYQ-027"]}]},{"DOI":"10.13039\/100006280","name":"Urology Care Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006280","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2023,8,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Accurate tumor detection is critical in cystoscopy to improve bladder cancer resection and decrease recurrence. Advanced deep learning algorithms hold the potential to improve the performance of standard white-light cystoscopy (WLC) in a noninvasive and cost-effective fashion. The purpose of this work is to develop a cost-effective, transformer-augmented deep learning algorithm for accurate detection of bladder tumors in WLC and to assess its performance on archived patient data.\n                    <jats:italic>Approach.<\/jats:italic>\n                    \u2018CystoNet-T\u2019, a deep learning-based bladder tumor detector, was developed with a transformer-augmented pyramidal CNN architecture to improve automated tumor detection of WLC. CystoNet-T incorporated the self-attention mechanism by attaching transformer encoder modules to the pyramidal layers of the feature pyramid network (FPN), and obtained multi-scale activation maps with global features aggregation. Features resulting from context augmentation served as the input to a region-based detector to produce tumor detection predictions. The training set was constructed by 510 WLC frames that were obtained from cystoscopy video sequences acquired from 54 patients. The test set was constructed based on 101 images obtained from WLC sequences of 13 patients.\n                    <jats:italic>Main results.<\/jats:italic>\n                    CystoNet-T was evaluated on the test set with 96.4 F1 and 91.4 AP (Average Precision). This result improved the benchmark of Faster R-CNN and YOLO by 7.3 points in F1 and 3.8 points in AP. The improvement is attributed to the strong ability of global attention of CystoNet-T and better feature learning of the pyramids architecture throughout the training. The model was found to be particularly effective in highlighting the foreground information for precise localization of the true positives while favorably avoiding false alarms\n                    <jats:italic>Significance.<\/jats:italic>\n                    We have developed a deep learning algorithm that accurately detects bladder tumors in WLC. Transformer-augmented AI framework promises to aid in clinical decision-making for improved bladder cancer diagnosis and therapeutic guidance.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ace499","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T04:48:23Z","timestamp":1691383703000},"page":"165013","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Tumor detection under cystoscopy with transformer-augmented deep learning algorithm"],"prefix":"10.1088","volume":"68","author":[{"given":"Xiao","family":"Jia","sequence":"first","affiliation":[{"name":"Shandong University"},{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugene","family":"Shkolyar","sequence":"additional","affiliation":[{"name":"Stanford University,"},{"name":"VA Palo Alto Health Care System, Palo Alto, CA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark A","family":"Laurie","sequence":"additional","affiliation":[{"name":"Stanford University"},{"name":"Stanford University,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Okyaz","family":"Eminaga","sequence":"additional","affiliation":[{"name":"Stanford University,"},{"name":"VA Palo Alto Health Care System, Palo Alto, CA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph C","family":"Liao","sequence":"additional","affiliation":[{"name":"Stanford University,"},{"name":"VA Palo Alto Health Care System, Palo Alto, CA,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xing","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"key":"pmbace499bib1","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1109\/tmi.2017.2664042","type":"journal-article","article-title":"Comparative validation of polyp detection methods in video colonoscopy: Results from the MICCAI 2015 endoscopic vision challenge","volume":"36","author":"Bernal","year":"2017","journal-title":"IEEE Trans. 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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":"2023-03-23","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-07-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-08-07","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}