Weakly Supervised Multiple Instance Learning Model With Generalization Ability for Clinical Adenocarcinoma Screening on Serous Cavity Effusion Pathology.

Accurate and rapid screening of adenocarcinoma cells in serous cavity effusion is vital in diagnosing the stage of metastatic tumors and providing prompt medical treatment. However, it is often difficult for pathologists to screen serous cavity effusion. Fixed agglutination cell block can help to improve diagnostic sensitivity in malignant tumor cells through analyzing larger volumes of serous cavity effusion, although it could accordingly lead to screening of more cells for pathologists. With the advent of whole slide imaging and development of artificial intelligence, advanced deep learning models are expected to assist pathologists in improving diagnostic efficiency and accuracy. In this study, so far as we know, it is the first time to use cell block technology combined with a proposed weakly supervised deep learning model with multiple instance learning method to screen serous adenocarcinoma. The comparative experiments were implemented through 5-fold cross-validation, and the results demonstrated that our proposed model not only achieves state-of-the-art performance under weak supervision while balancing the number of learnable parameters and computational costs and reduces the workload of pathologists but also presents a quantitative and interpretable cellular pathologic scene of serous adenocarcinoma with superior interpretability and strong generalization capability. The performances and features of the model indicate its effectiveness in the rapid screening and diagnosis of serous cavity effusion and its potential in broad clinical application prospects, eg, in precision medical applications. Moreover, the constructed 2 real-world pathologic data sets would be the first public whole slide imaging data sets of serous cavity effusion with adenocarcinoma based on cell block sections, which can help assist colleagues.