Seesawad N.Ittichaiwong P.Sudhawiyangkul T.Sawangjai P.Thuwajit P.Boonsakan P.Sripodok S.Veerakanjana K.Charngkaew K.Pongpaibul A.Angkathunyakul N.Hnoohom N.Yuenyong S.Thuwajit C.Wilaiprasitporn T.Mahidol University2024-06-082024-06-082024-01-01IEEE Open Journal of Engineering in Medicine and Biology (2024)https://repository.li.mahidol.ac.th/handle/20.500.14594/98649<italic>Background:</italic> Deep learning models for patch classification in whole-slide images (WSI) have shown promise in assisting follicular lymphoma grading. However, these models often require pathologists to identify centroblasts and manually provide refined labels for model optimization. <italic>Objective:</italic> To address this limitation, we propose <italic>PseudoCell</italic>, an object detection framework for automated centroblast detection in WSI, eliminating the need for extensive pathologist&#x0027;s refined labels. <italic>Methods:</italic> <italic>PseudoCell</italic> leverages a combination of pathologist-provided centroblast labels and pseudo-negative labels generated from undersampled false-positive predictions based on cell morphology features. This approach reduces the reliance on time-consuming manual annotations. <italic>Results:</italic> Our framework significantly reduces the workload for pathologists by accurately identifying and narrowing down areas of interest containing centroblasts. Depending on the confidence threshold, <italic>PseudoCell</italic> can eliminate 58.18-99.35&#x0025; of irrelevant tissue areas on WSI, streamlining the diagnostic process. <italic>Conclusion:</italic> This study presents <italic>PseudoCell</italic> as a practical and efficient prescreening method for centroblast detection, eliminating the need for refined labels from pathologists. The discussion section&#x00A0;provides detailed guidance for implementing <italic>PseudoCell</italic> in clinical practice.EngineeringArticleSCOPUS10.1109/OJEMB.2024.34073512-s2.0-8519483495926441276