Review on Computer Vision in Gastric Cancer: Potential Efficient Tools for Diagnosis
It addresses the challenge of rapid diagnosis for clinical doctors, but is incremental as it synthesizes existing advances rather than introducing new methods.
This review paper examines recent computer vision methods for gastric cancer diagnosis, highlighting how various techniques for data generation, feature extraction, classification, and segmentation can improve diagnostic accuracy and efficiency.
Rapid diagnosis of gastric cancer is a great challenge for clinical doctors. Dramatic progress of computer vision on gastric cancer has been made recently and this review focuses on advances during the past five years. Different methods for data generation and augmentation are presented, and various approaches to extract discriminative features compared and evaluated. Classification and segmentation techniques are carefully discussed for assisting more precise diagnosis and timely treatment. For classification, various methods have been developed to better proceed specific images, such as images with rotation and estimated real-timely (endoscopy), high resolution images (histopathology), low diagnostic accuracy images (X-ray), poor contrast images of the soft-tissue with cavity (CT) or those images with insufficient annotation. For detection and segmentation, traditional methods and machine learning methods are compared. Application of those methods will greatly reduce the labor and time consumption for the diagnosis of gastric cancers.