ArticleBioMed research international2021
CST: A Multitask Learning Framework for Colorectal Cancer Region Mining Based on Transformer.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Deep multi-task learning framework for gastrointestinal lesion-aided diagnosis and severity estimation.Scientific reports · 2025Article
- Transformers for colorectal cancer segmentation in CT imaging.International journal of computer assisted radiology and surgery · 2024Article
- CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images.Bioengineering (Basel, Switzerland) · 2024Article
- Preoperative CT-based deep learning model for predicting overall survival in patients with high-grade serous ovarian cancer.Frontiers in oncology · 2022Article
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Authors and funding
6 authors.
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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.
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