Here are some slides I made to present this CVPR 2019 paper in our reading group:

The paper proposes using Generative Adversarial Networks (GANs) to augment the dataset with high quality synthetic liver lesion images in order to improve the CNN classification performance for medical image classification. The authors use limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The liver lesions vary considerably in shape, contrast and size, and also present intra-class variability. ...

In this paper, the authors proposed a fully convolutional neural network architecture for biomedical image segmentation which overcame the limitations of the contemporary algorithms. Unlike other popular algorithms then, the proposed network did not suffer from the redundancy arising out of overlapping training patches. Moreover, the authors eliminate the trade-off between localization accuracy and the use of context and state that “good localization and the use of context are possible at the same time”. ...

As the title suggests, this paper studies various different deep convolutional neural network architectures and various techniques to use these CNNs for CADe (Computer Aided Detection) tasks. With the tremendous popularity of CNN models, the authors state that the “tremendous” success of CNNs in medical image tasks has been primarily using three techniques. ...