Computer aided endoscope diagnosis via weakly labeled data mining

In comparison to most computer aided endoscope diagnosis methods using pixel-wise groundtruth by physicians manually, it is easy to get lots of endoscope images with corresponding diagnostic reports. In this paper, we intend to mine pixel-wise label information from these reports with weak frame-level labels automatically. To achieve this, we formulate our computer aided diagnosis problem as a Multiple Instance Learning (MIL) issue, where we represent each image as superpixels. Each image and each superpixel is cast as bag and instance, respectively. We then evaluate and select the most positive instances from positive bags automatically which helps us transform the frame-level classification problem into a standard supervised learning problem. In the experiment, we build a new gastroscopic image dataset with more than 3000 weakly labeled images, and ours outperforms the state-of-the-art methods, which verifies the effectiveness of our model.