1. Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging
- Author
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Xiongbiao Luo, Li-Yuan Lu, Ping Lin, Yu-Heng Shi, Su-Yu Chen, Jian-Hua Chen, Yu-Wen Cai, Chen Chen, Fang-Fen Dong, and Yu-Shan Xue
- Subjects
Artificial intelligence ,medicine.medical_specialty ,Lesion detection ,business.industry ,Deep learning ,education ,Minireviews ,General Medicine ,Colorectal cancer ,Image analysis ,Colorectal lesions ,Endoscopic ,Medicine ,Radiology ,business ,Pathological - Abstract
Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.
- Published
- 2021
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