AI-assisted Early Detection of Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT
This addresses the critical problem of late diagnosis in PDAC for patients, though it appears incremental as it builds on existing detection methods with specific optimizations.
The paper tackles early detection of pancreatic ductal adenocarcinoma (PDAC) on CT scans using a coarse-to-fine AI approach, achieving first place in a challenge with an AUROC of 0.9263 and AP of 0.7243.
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.