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

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.

Conference Presentation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available