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Rongxiao Tang

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Accurate pathological patch classification with regression

Rongxiao Tang, Bo Yuan, Zhenhua Guo

Proceedings Volume 11603, 1160312 (2021) https://doi.org/10.1117/12.2580898

KEYWORDS: Cancer, Tumors, Tumor growth modeling, Tissues, Image resolution, Image analysis, Computing systems

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Histopathological image analysis is important for cancer diagnosis. However, current computer cannot support the end-toend prediction of whole slide images (WSI) with gigapixel resolution. Most of current works slice high-resolution WSI into patches and classify them as tumor/normal tissues. However, the classification based on patches only uses category labels according to the proportion of cancer cells. For example, if a patch contains more than 50% cancer cells, its label is “tumor”, otherwise it is “normal”. Obviously, although this scheme can achieve good classification performance, it is unreasonable because doctors do not simply classify patches by whether the proportion of cancer cells is above 50% or not. If the ratio information of cancer cells in the patches can be fully utilized, the rationality and accuracy can be improved. In this article, we firstly notice cancer cell ratio information and propose a new model structure, combining classification and regression modules. We introduce a regression branch with reference to the fully connected layer structure of the classification model, and then combine the regression branch with the classification branch. When classifying the patches, the regression branch predicts the proportion of cancer cells, which can assist classification branch to achieve better patchlevel classification. In addition, we find that the baseline classification branch and the regression-assisted classification branch have different properties for tumor/normal patches. Combining the two branches for final prediction can further improve the classification performance. We apply our method to tumor/normal prediction on two large datasets and achieve better classification performance than state-of-the-art methods. Source code is available at https://github.com/ICLAB/ RACN.

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