{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T04:04:24Z","timestamp":1747454664991,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685960","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,15]]},"abstract":"<jats:p>The study underscores the importance of addressing biases in medical AI models to improve fairness, generalizability, and clinical utility. In this paper, we present a novel framework that combines Explainable AI (XAI) with image darkness assessment to detect and mitigate bias in cervical histology image classification. Four deep learning architectures were employed\u2014AlexNet, ResNet-50, EfficientNet-B0, and DenseNet-121\u2014with EfficientNet-B0 demonstrating the highest accuracy post-mitigation. Grad-CAM and saliency maps were used to identify biases in the models\u2019 predictions. After applying brightness normalisation and synthetic data augmentation, the models shifted focus toward clinically relevant features, improving both accuracy and fairness. Statistical analysis using ANOVA confirmed a reduction in the influence of image darkness on model predictions after mitigation, as evidenced by a decrease in the F-statistic from 120.79 to 14.05, indicating improved alignment of the models with clinically relevant features.<\/jats:p>","DOI":"10.3233\/shti250302","type":"book-chapter","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:53:15Z","timestamp":1747385595000},"source":"Crossref","is-referenced-by-count":0,"title":["Bias Detection in Histology Images Using Explainable AI and Image Darkness Assessment"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3458-8730","authenticated-orcid":false,"given":"Inna","family":"Skarga-Bandurova","sequence":"first","affiliation":[{"name":"Oxford Brookes University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0993-9458","authenticated-orcid":false,"given":"Golshid","family":"Sharifnia","sequence":"additional","affiliation":[{"name":"Oxford Brookes University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7561-7484","authenticated-orcid":false,"given":"Tetiana","family":"Biloborodova","sequence":"additional","affiliation":[{"name":"G.E. Pukhov Institute for Modelling in Energy Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Intelligent Health Systems \u2013 From Technology to Data and Knowledge"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250302","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:53:16Z","timestamp":1747385596000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"ISBN":["9781643685960"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250302","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]}}}