Early and 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 focused on advances during the past five years. Different methods for data generation and augmentation have been 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. Application of those methods will greatly reduce the labor and time consumed for the diagnosis of gastric cancers.
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