Abstract
In digital pathology, cancer grading has been widely studied by utilizing hand-crafted features and advanced machine learning and deep learning methods. In most of such studies, cancer grading has been formulated as a multi-class categorical classification problem, likely overlooking the relationship among different cancer grades. Herein, we propose a ranking-based deep neural network for cancer grading in pathology images. Utilizing deep neural networks, pathology images are mapped into a latent space. Built based upon a triplet loss, a ranking loss is devised to maximize the inter-class distance among cancer grades in the latent space with respect to the aggressiveness of cancer, leading to the correct ordering or rank of pathology images. To evaluate the proposed method, a number of colorectal pathology images have been employed. The experimental results demonstrate that the proposed approach is capable of predicting cancer grades with high accuracy, outperforming the deep neural networks without the ranking loss.
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.NRF-2021R1A4A1031864).
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Le Vuong, T.T., Kim, K., Song, B., Kwak, J.T. (2021). Ranking Loss: A Ranking-Based Deep Neural Network for Colorectal Cancer Grading in Pathology Images. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_52
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