Deep multiple-instance learning for abnormal cell detection in cervical histopathology images.

Cervical cancer is a disease of significant concern affecting women's health worldwide. Early detection of and treatment at the precancerous stage can help reduce mortality. High-grade cervical abnormalities and precancer are confirmed using microscopic analysis of cervical histopathology. However, manual analysis of cervical biopsy slides is time-consuming, needs expert pathologists, and suffers from reader variability errors. Prior work in the literature has suggested using automated image analysis algorithms for analyzing cervical histopathology images captured with the whole slide digital scanners (e.g., Aperio, Hamamatsu, etc.). However, whole-slide digital tissue scanners with good optical magnification and acceptable imaging quality are cost-prohibitive and difficult to acquire in low and middle-resource regions. Hence, the development of low-cost imaging systems and automated image analysis algorithms are of critical importance. Motivated by this, we conduct an experimental study to assess the feasibility of developing a low-cost diagnostic system with the H&E stained cervical tissue image analysis algorithm. In our imaging system, the image acquisition is performed by a smartphone affixing it on the top of a commonly available light microscope which magnifies the cervical tissues. The images are not captured in a constant optical magnification, and, unlike whole-slide scanners, our imaging system is unable to record the magnification. The images are mega-pixel images and are labeled based on the presence of abnormal cells. In our dataset, there are total 1331 (train: 846, validation: 116 test: 369) images. We formulate the classification task as a deep multiple instance learning problem and quantitatively evaluate the classification performance of four different types of multiple instance learning algorithms trained with five different architectures designed with varying instance sizes. Finally, we designed a sparse attention-based multiple instance learning framework that can produce a maximum of 84.55% classification accuracy on the test set.