Pancreatic ductal adenocarcinoma (PDAC) is one of the most common and aggressive types of pancreatic cancer. However, due to the lack of early and disease-specific symptoms, most patients with PDAC are diagnosed at an advanced disease stage. Consequently, early PDAC detection is crucial for improving patients' quality of life and expanding treatment options. In this work, we develop a coarse-to-fine approach to detect PDAC on contrast-enhanced CT scans. First, we localize and crop the region of interest from the low-resolution images, and then segment the PDAC-related structures at a finer scale. Additionally, we introduce two strategies to further boost detection performance: (1) a data-splitting strategy for model ensembling, and (2) a customized post-processing function. We participated in the PANORAMA challenge and ranked 1st place for PDAC detection with an AUROC of 0.9263 and an AP of 0.7243. Our code and models are publicly available at https://github.com/han-liu/PDAC_detection.
View on arXiv@article{liu2025_2503.10068,
title={ AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT },
author={ Han Liu and Riqiang Gao and Sasa Grbic },
journal={arXiv preprint arXiv:2503.10068},
year={ 2025 }
}