Detection of gastric cancer risk from X-ray images via patch-based convolutional neural network

This paper presents a novel detection method of gastric cancer risk from X-ray images using the patch-based Convolutional Neural Network (CNN). Our method enables the training of the patch-based CNN which can accurately detect gastric cancer risk even though there is only the image-level ground truth. Furthermore, the proposed method can extract a feature vector that can represent the whole of symptoms associated with the presence or absence of the risk. Specifically, the proposed method selects the patches related to their true risk via the CNN, and it is the most innovative contribution of our method. Moreover, we extract the feature vector by applying the Bag-of-Feature representation to the output values from the CNN's intermediate layer obtained from the selected patches. Finally, the detection of gastric cancer risk is performed by inputting the extracted feature vector into Support Vector Machine. Experimental results confirm that the proposed method outperforms a previously reported method that combines the detection results obtained from X-ray images taken from multiple angles even though the proposed method only uses an X-ray image taken from a single angle, and we can achieve a higher performance than that of doctors.