Minimizing the intra-pathologist disagreement for tumor bud detection on H&E images using weakly supervised learning

Thomas E. Tavolara; Wei Chen; Wendy L. Frankel; Metin N. Gurcan; M. Khalid Khan Niazi

doi:10.1117/12.2653887

6 April 2023 Minimizing the intra-pathologist disagreement for tumor bud detection on H&E images using weakly supervised learning

Thomas E. Tavolara, Wei Chen, Wendy L. Frankel, Metin N. Gurcan, M. Khalid Khan Niazi

Author Affiliations +

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 1247113 (2023) https://doi.org/10.1117/12.2653887
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Tumor budding (TB) is defined as a cluster of one to four tumor cells at the tumor invasive front. Though promising as a prognostic factor for colorectal cancer, its routine clinical use is hampered by high inter- and intra- observer disagreement on routine H&E staining. Pan-cytokeratin immunohistochemical staining increases agreement but is costly, non-routine, and may yield false tumor buds (false positives). This makes the development of automatic algorithms to identify TB difficult. Therefore, we propose a weakly-supervised method that does not require strictly accurate tissue level annotations and is resilient to false positives. Our database consists of 29 H&E whole slide images. TB and nontumor ROIs were generated by cropping 512x512 regions around annotated tumor buds and within annotated non-tumor regions, respectively. Attention-based multiple instance learning was applied to identify ROIs containing tumor buds. This resulted in a precision of 0.9477 ± 0.0516, recall of 0.9131 ± 0.0568, and AUC of 0.9482 ± 0.0679 on an external dataset. These results provide preliminary evidence for the feasibility of our method to identify tumor buds accurately.

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Citation Download Citation

Thomas E. Tavolara, Wei Chen, Wendy L. Frankel, Metin N. Gurcan, and M. Khalid Khan Niazi "Minimizing the intra-pathologist disagreement for tumor bud detection on H&E images using weakly supervised learning", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 1247113 (6 April 2023); https://doi.org/10.1117/12.2653887

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