1 research outputs found
Review on Computer Vision in Gastric Cancer: Potential Efficient Tools for Diagnosis
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