Uncertainty Estimation for Tumor Prediction with Unlabeled Data

Juyoung Yun, Shahira Abousamra, Chen Li, Rajarsi Gupta, Tahsin Kurc, Dimitris Samaras, Alison Van Dyke, Joel Saltz, Chao Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6946-6954

Abstract

Estimating uncertainty of a neural network is crucial in providing transparency and trustworthiness. In this paper we focus on uncertainty estimation for digital pathology prediction models. To explore the large amount of unlabeled data in digital pathology we propose to adopt novel learning method that can fully exploit unlabeled data. The proposed method achieves superior performance compared with different baselines including the celebrated Monte-Carlo Dropout. Closeup inspection of uncertain regions reveal insight into the model and improves the trustworthiness of the models.

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[bibtex]
@InProceedings{Yun_2024_CVPR, author = {Yun, Juyoung and Abousamra, Shahira and Li, Chen and Gupta, Rajarsi and Kurc, Tahsin and Samaras, Dimitris and Van Dyke, Alison and Saltz, Joel and Chen, Chao}, title = {Uncertainty Estimation for Tumor Prediction with Unlabeled Data}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2024}, pages = {6946-6954} }