Quantifying Boundary Reliability in Endoscopic Ultrasound Pancreas Segmentation via Evidential Deep Learning

Hana Kim, Khalid Ahmed Husain, Jianing Li, Venkata S Akshintala, S. Swaroop Vedula, Marcia Irene Canto, Craig Jones
Proceedings of the 11th Machine Learning for Healthcare Conference, PMLR 340:886-912, 2026.

Abstract

Pancreatic cancer has a very high mortality rate, making its early detection essential for improving patient survival. Endoscopic Ultrasound (EUS) offers spatial resolution necessary for early-stage screening, but it is sensitive to artifacts, noise, and low contrast, which make automated image analysis challenging. We present an uncertainty-aware framework for EUS pancreas segmentation, benchmarking four U-Net variants across multi-institutional datasets. nnU-Net achieved the strongest performance with a Dice score of $0.811 \pm 0.122$ on our internal data. We introduce Inside Ground Truth Annulus Percentage (IGTP), a clinically-motivated metric revealing that models systematically fail to adhere to distal pancreatic margins where acoustic shadowing is severe. To localize these failures, we trained an evidential nnU-Net that parameterizes pixel-wise class probabilities using a Dirichlet distribution and learns evidence through an expected cross-entropy objective with KL regularization. The learned Dirichlet parameters enable the estimation of aleatoric and epistemic uncertainty in a single forward pass. Quantitative analysis showed that higher epistemic uncertainty was associated with poorer segmentation and boundary adherence and provided a predictive signal to identify low-performing cases. Together, the proposed IGTP metric and trained evidential framework establish a trustworthy foundation for AI-assisted EUS screening.

Cite this Paper

BibTeX
@InProceedings{pmlr-v340-kim26b, title = {Quantifying Boundary Reliability in Endoscopic Ultrasound Pancreas Segmentation via Evidential Deep Learning}, author = {Kim, Hana and Husain, Khalid Ahmed and Li, Jianing and Akshintala, Venkata S and Vedula, S. Swaroop and Canto, Marcia Irene and Jones, Craig}, booktitle = {Proceedings of the 11th Machine Learning for Healthcare Conference}, pages = {886--912}, year = {2026}, editor = {Krishnan, Rahul G. and van Amsterdam, Wouter A. C. and Chopra, Sumit and Overgaard, Shauna and Hughes, Michael and Ötleş, Erkin and Shen, Yiqiu and Shanmugam, Divya and Nayan, Madhur and Engelhard, Matthew and Fackler, Jim and Oberst, Michael}, volume = {340}, series = {Proceedings of Machine Learning Research}, month = {12--14 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v340/main/assets/kim26b/kim26b.pdf}, url = {https://proceedings.mlr.press/v340/kim26b.html}, abstract = {Pancreatic cancer has a very high mortality rate, making its early detection essential for improving patient survival. Endoscopic Ultrasound (EUS) offers spatial resolution necessary for early-stage screening, but it is sensitive to artifacts, noise, and low contrast, which make automated image analysis challenging. We present an uncertainty-aware framework for EUS pancreas segmentation, benchmarking four U-Net variants across multi-institutional datasets. nnU-Net achieved the strongest performance with a Dice score of $0.811 \pm 0.122$ on our internal data. We introduce Inside Ground Truth Annulus Percentage (IGTP), a clinically-motivated metric revealing that models systematically fail to adhere to distal pancreatic margins where acoustic shadowing is severe. To localize these failures, we trained an evidential nnU-Net that parameterizes pixel-wise class probabilities using a Dirichlet distribution and learns evidence through an expected cross-entropy objective with KL regularization. The learned Dirichlet parameters enable the estimation of aleatoric and epistemic uncertainty in a single forward pass. Quantitative analysis showed that higher epistemic uncertainty was associated with poorer segmentation and boundary adherence and provided a predictive signal to identify low-performing cases. Together, the proposed IGTP metric and trained evidential framework establish a trustworthy foundation for AI-assisted EUS screening.} }
Endnote
%0 Conference Paper %T Quantifying Boundary Reliability in Endoscopic Ultrasound Pancreas Segmentation via Evidential Deep Learning %A Hana Kim %A Khalid Ahmed Husain %A Jianing Li %A Venkata S Akshintala %A S. Swaroop Vedula %A Marcia Irene Canto %A Craig Jones %B Proceedings of the 11th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2026 %E Rahul G. Krishnan %E Wouter A. C. van Amsterdam %E Sumit Chopra %E Shauna Overgaard %E Michael Hughes %E Erkin Ötleş %E Yiqiu Shen %E Divya Shanmugam %E Madhur Nayan %E Matthew Engelhard %E Jim Fackler %E Michael Oberst %F pmlr-v340-kim26b %I PMLR %P 886--912 %U https://proceedings.mlr.press/v340/kim26b.html %V 340 %X Pancreatic cancer has a very high mortality rate, making its early detection essential for improving patient survival. Endoscopic Ultrasound (EUS) offers spatial resolution necessary for early-stage screening, but it is sensitive to artifacts, noise, and low contrast, which make automated image analysis challenging. We present an uncertainty-aware framework for EUS pancreas segmentation, benchmarking four U-Net variants across multi-institutional datasets. nnU-Net achieved the strongest performance with a Dice score of $0.811 \pm 0.122$ on our internal data. We introduce Inside Ground Truth Annulus Percentage (IGTP), a clinically-motivated metric revealing that models systematically fail to adhere to distal pancreatic margins where acoustic shadowing is severe. To localize these failures, we trained an evidential nnU-Net that parameterizes pixel-wise class probabilities using a Dirichlet distribution and learns evidence through an expected cross-entropy objective with KL regularization. The learned Dirichlet parameters enable the estimation of aleatoric and epistemic uncertainty in a single forward pass. Quantitative analysis showed that higher epistemic uncertainty was associated with poorer segmentation and boundary adherence and provided a predictive signal to identify low-performing cases. Together, the proposed IGTP metric and trained evidential framework establish a trustworthy foundation for AI-assisted EUS screening.
APA
Kim, H., Husain, K.A., Li, J., Akshintala, V.S., Vedula, S.S., Canto, M.I. & Jones, C.. (2026). Quantifying Boundary Reliability in Endoscopic Ultrasound Pancreas Segmentation via Evidential Deep Learning. Proceedings of the 11th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 340:886-912 Available from https://proceedings.mlr.press/v340/kim26b.html.

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