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.
Highlights
Colorectal cancer is a common malignancy tumour worldwide, which has ranked the third position as the most common cancer and the second cause of cancer-related deaths worldwide
We propose a novel transfer learning protocol, called CST, which 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
The main aims of this dataset are to collect for colorectal cancer region detection and segmentation, and to follow this aim, we construct the framework in this manuscript to perform them and prepared for the clinical applications
Summary
Colorectal cancer is a common malignancy tumour worldwide, which has ranked the third position as the most common cancer and the second cause of cancer-related deaths worldwide. In China, there are more than 480,000 new cases with a higher than 30% death percentage in 2020, which increases the incidence and mortality rates rank following lung cancer [2]. Occult blood examination and medical images were employed for clinical detection and diagnosis. These methods exhibited a productive approach for the early colorectal cancer diagnosis and can improve the survival of these patients [3]. Mere blood examination and colonoscopy inspection could not reveal the biological morphology and tumour statutes [4, 5]. In the past decades, imaging approaches such as computed tomography (CT) and magnetic resonance imaging (MRI)
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