Bibliographic Details
| Title: |
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans |
| Authors: |
Yuan, Mingze, Xia, Yingda, Chen, Xin, Yao, Jiawen, Wang, Junli, Qiu, Mingyan, Dong, Hexin, Zhou, Jingren, Dong, Bin, Lu, Le, Zhang, Li, Liu, Zaiyi, Zhang, Ling |
| Publication Year: |
2023 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| More Details: |
Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer detection. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner. Our model incorporates learnable clusters that encode the texture and shape prototypes of gastric cancer, utilizing self- and cross-attention to interact with convolutional features. In our experiments, the proposed method achieves a sensitivity of 85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal. In comparison, two radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We also obtain a specificity of 97.7% on an external test set with 903 normal cases. Our approach performs comparably to established state-of-the-art gastric cancer screening tools like blood testing and endoscopy, while also being more sensitive in detecting early-stage cancer. This demonstrates the potential of our approach as a novel, non-invasive, low-cost, and accurate method for opportunistic gastric cancer screening.
Comment: MICCAI 2023 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2307.04525 |
| Accession Number: |
edsarx.2307.04525 |
| Database: |
arXiv |
