Salient Patch Based NAS for Grading of Colorectal Cancer Histology Images
Hang Meng, Fei Li, Zhen Yang, Qiang Zhu, Chaogang Fan, Shu Zhan
Proceedings of the 13th International Multi-Conference on Complexity, Informatics and Cybernetics: IMCIC 2022, Vol. II, pp. 146-152 (2022); https://doi.org/10.54808/IMCIC2022.02.146
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The 13th International Multi-Conference on Complexity, Informatics and Cybernetics: IMCIC 2022
Virtual Conference March 8 - 11, 2022 Proceedings of IMCIC 2022 ISSN: 2771-5914 (Print) ISBN (Volume II): 978-1-950492-61-9 (Print) |
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Abstract
Digital histopathological tissue images are gold material for cancer diagnosis and grades. Convolutional Neural Networks (CNNs) are state-of-the-art models in many image classification tasks. However, considering the limited computing resources, it is not practical to directly train CNNs on gigapixel resolution histopathological tissue images. Therefore, the entire histopathological image is commonly divided into a grid of patches and then fed each patch to CNNs, but not all patches are necessary for specific tasks. Besides, although CNNs are excellent image classifiers, many pre-trained models are based on natural images. Neural Architecture Search (NAS) can realize automatic search, construction, and training specific problem-oriented networks. We present salient patch based NAS for grading colorectal cancer (CRC) histology images with these motivations. The proposed framework first applies the classic classification network to extract significant patches from the whole image, then use the network obtained by NAS to grade the patches, and finally aggregates the patch-level predictions to generate the entire CRC histological image label. Our experiments on microscopic colorectal tissue images demonstrate that the proposed method is sufficient to obtain more accurate classification results compared to its counterparts. We also conducted a comprehensive analysis of the different scenarios of the proposed method.
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