A Weakly Supervised Tissue-level Approach for Histological Differentiation of Gastric Adenocarcinomas

How to cite: Hyeon, D.; Kim, D.; Ryu, W.; Sung, S.H.; Bhattacharjee, S.; Kim, C.; Cho, N.; Moon, D.; Lee, D.; Kim, H.; Shin, H.; Lee, W.; Cho, M.; Choi, H.; Kang, H. A Weakly Supervised Tissue-level Approach for Histological Differentiation of Gastric Adenocarcinomas. Preprints 2022, 2022080080. https://doi.org/10.20944/preprints202208.0080.v1 Hyeon, D.; Kim, D.; Ryu, W.; Sung, S.H.; Bhattacharjee, S.; Kim, C.; Cho, N.; Moon, D.; Lee, D.; Kim, H.; Shin, H.; Lee, W.; Cho, M.; Choi, H.; Kang, H. A Weakly Supervised Tissue-level Approach for Histological Differentiation of Gastric Adenocarcinomas. Preprints 2022, 2022080080. https://doi.org/10.20944/preprints202208.0080.v1

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MDPI and ACS Style

Hyeon, D.; Kim, D.; Ryu, W.; Sung, S.H.; Bhattacharjee, S.; Kim, C.; Cho, N.; Moon, D.; Lee, D.; Kim, H.; Shin, H.; Lee, W.; Cho, M.; Choi, H.; Kang, H. A Weakly Supervised Tissue-level Approach for Histological Differentiation of Gastric Adenocarcinomas. Preprints 2022, 2022080080. https://doi.org/10.20944/preprints202208.0080.v1

APA Style

Hyeon, D., Kim, D., Ryu, W., Sung, S.H., Bhattacharjee, S., Kim, C., Cho, N., Moon, D., Lee, D., Kim, H., Shin, H., Lee, W., Cho, M., Choi, H., & Kang, H. (2022). A Weakly Supervised Tissue-level Approach for Histological Differentiation of Gastric Adenocarcinomas. Preprints. https://doi.org/10.20944/preprints202208.0080.v1

Chicago/Turabian Style

Hyeon, D., Heung-Kook Choi and Haeyoun Kang. 2022 "A Weakly Supervised Tissue-level Approach for Histological Differentiation of Gastric Adenocarcinomas" Preprints. https://doi.org/10.20944/preprints202208.0080.v1

Abstract

Histologically poor differentiation is associated with lymph node metastasis. Thus, pathological evaluation of biopsy specimens is crucial when treating stomach cancers. Deep learning of WSIs is challenging because the images are enormous. Given the computing limitations, patch-level supervised learning methods have been proposed. However, valuable information is lost when dividing WSIs into smaller patches. Another drawback is the need for pixel-level annotation by a pathologist. It is acceptable to differentiate, i.e., grade, gastric cancer at the holistic tissue level (i.e., under low magnification). We developed a weakly supervised learning technique for tissue-level gastric adenocarcinoma histological differentiation (well-to-moderately or poorly differentiated) and applied global reasoning to tissue-level features. The tissue-level AUROCs of the histological differentiation classifiers were 0.953, 0.969, and 0.943, respectively when data from five hospitals were subjected to threefold cross-validation. Comparison of the Grad-CAM heatmaps of the trained classifier and the pathologists’ annotations confirmed that our weakly supervised model exhibited performed well.

Keywords

gastric cancer; deep learning; digital pathology; lymph node metastasis

Subject

Computer Science and Mathematics, Mathematical and Computational Biology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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