Affiliation:
1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2. Department of Laboratory and Diagnosis, Changhai Hospital, Navy Medical University, Shanghai 200433, China
Abstract
Colorectal cancer is a high death rate cancer until now; from the clinical view, the diagnosis of the tumour region is critical for the doctors. But with data accumulation, this task takes lots of time and labor with large variances between different doctors. With the development of computer vision, detection and segmentation of the colorectal cancer region from CT or MRI image series are a great challenge in the past decades, and there still have great demands on automatic diagnosis. In this paper, we proposed a novel transfer learning protocol, called CST, that is, a union framework for colorectal cancer region detection and segmentation task based on the transformer model, which effectively constructs the cancer region detection and its segmentation jointly. To make a higher detection accuracy, we incorporate an autoencoder-based image-level decision approach that leverages the image-level decision of a cancer slice. We also compared our framework with one-stage and two-stage object detection methods; the results show that our proposed method achieves better results on detection and segmentation tasks. And this proposed framework will give another pathway for colorectal cancer screen by way of artificial intelligence.
Funder
National Key Research and Development Program of China
Subject
General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine
Cited by
5 articles.
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