Deep Learning Techniques for Histopathology Image Classification: A Comprehensive Review

10.6919/ICJE.202205_8(5).0116

Zhuolin Liu；Kun Liu

Histopathological Image ； Deep Learning ； Semi-supervised Deep Learning ； Computer-aided Diagnosis System

International Core Journal of Engineering

8卷5期（2022 / 05 / 01）

897 - 906

英文

Histopathology detection is a critical clinical step in determining whether a patient has cancer and the type of cancer, and its results directly determine how the physician treats the patient. However, the detection of histopathological images is highly dependent on the experience of histopathologists and the amount of work involved in the detecting. Automated evaluation of cancerous tissue by artificial intelligence (AI) systems may help pathologists reduce workload and prevent subjective bias in cancer diagnosis. The purpose of this study is to review new applications of deep learning in histopathology classification. This review discusses both supervised learning and semi-supervised learning. This paper also introduces convolutional neural network (CNN) and semi-supervised techniques applied to medical images mainly. Finally, the paper summarizes the possible challenges of deep learning in histopathology classification.

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