2018/07/09 by Zhuotun Zhu, Yingda Xia, Zhu, Zhuotun +8 · 2 citations
Computer Science · Medicine · #AI in cancer detection #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Pancreatic and Hepatic Oncology Research #cs.CV
paper · pdf · doi:10.48550/arxiv.1807.02941
Accepted by MICCAI 2019, 4 figures, 2 tables, 9 pages
openalex publication_date 2018/07/09 · openalex created_date 2018/07/19 · arxiv created 2019/08/09 · arxiv updated 2019/08/09 · openalex updated_date 2026/07/28
We propose an intuitive approach of detecting pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, by checking abdominal CT scans. Our idea is named multi-scale segmentation-for-classification, which classifies volumes by checking if at least a sufficient number of voxels is segmented as tumors, by which we can provide radiologists with tumor locations. In order to deal with tumors with different scales, we train and test our volumetric segmentation networks with multi-scale inputs in a coarse-to-fine flowchart. A post-processing module is used to filter out outliers and reduce false alarms. We collect a new dataset containing 439 CT scans, in which 136 cases were diagnosed with PDAC and 303 cases are normal, which is the largest set for PDAC tumors to the best of our knowledge. To offer the best trade-off between sensitivity and specificity, our proposed framework reports a sensitivity of 94.1% at a specificity of 98.5%, which demonstrates the potential to make a clinical impact.