Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 6; pp. 1643 - 1651
• Main Authors: Sun, Chunli, Xu, Ao, Liu, Dong, Xiong, Zhiwei, Zhao, Feng, Ding, Weiping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-06-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Annotations; Biomedical imaging; Cancer; Classification; Databases, Factual; Deep Learning; Diagnosis; Early Detection of Cancer; Feature extraction; Global labels; Histological Techniques; histopathological image analysis; Histopathology; Humans; Image acquisition; Image analysis; Image classification; Image color analysis; Image Interpretation, Computer-Assisted - methods; Image processing; Labels; Liver; Liver - diagnostic imaging; Liver - pathology; Liver cancer; Liver Neoplasms - classification; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - pathology; Machine learning; Medical imaging; Morbidity; Morphology; multiple instance learning; Training; Transfer learning
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2019.2949837

Liver cancer is a leading cause of cancer deaths worldwide due to its high morbidity and mortality. Histopathological image analysis (HIA) is a crucial step in the early diagnosis of liver cancer and is routinely performed manually. However, this process is time-consuming, error-prone, and easily affected by the expertise of pathologists. Recently, computer-aided methods have been widely applied to medical image analysis; however, the current medical image analysis studies have not yet focused on the histopathological morphology of liver cancer due to its complex features and the insufficiency of training images with detailed annotations. This paper proposes a deep learning method for liver cancer histopathological image classification using only global labels. To compensate for the lack of detailed cancer region annotations in those images, patch features are extracted and fully utilized. Transfer learning is used to obtain the patch-level features and then combined with multiple-instance learning to acquire the image-level features for classification. The method proposed here solves the processing of large-scale images and training sample insufficiency in liver cancer histopathological images for image classification. The proposed method can distinguish and classify liver histopathological images as abnormal or normal with high accuracy, thus providing support for the early diagnosis of liver cancer.