Abstract
Breast cancer is one of the leading cause of cancer-related death worldwide. During the diagnosis of breast cancer, the histopathological assessment of Haemotoxylin and Eosin(H&E) stained slides provides important clinical values. By applying computer-aid diagnosis on whole-slide image(WSI), the efficiency and consistency of such assessment could be improved. In this paper, we propose a deep learning-based framework that classifies H&E stained WSIs into regions of normal tissue, benign lesion, in-situ carcinoma and invasive carcinoma. The framework utilizes both microscopy images and WSIs to train a patch classifier in two stages. The underlying classifier is based on Inception-Resnet-v2. This framework won both parts of the ICIAR2018 Grand Challenge on Breast Cancer Histology Images [4] competition, achieved a part A multiclass accuracy of 87% and part B score of 0.6929.
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We would like to thank the organizers of ICIAR2018 and BACH2018 who supported and organized this challenge.
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Kwok, S. (2018). Multiclass Classification of Breast Cancer in Whole-Slide Images. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_106
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DOI: https://doi.org/10.1007/978-3-319-93000-8_106
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