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Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.

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We propose a fully automated method for prostate segmentation using random forests (RFs) and graph cuts. A volume of interest (VOI) is automatically selected using supervoxel segmentation, and its subsequent classification using image features and RF classifiers. The VOIs probability map is generated using image and context features, and a second set of RF classifiers. The negative log-likelihood of the probability maps acts as the penalty cost in a second-order Markov random field cost function. Semantic information from the second set of RF classifiers is an important measure of each feature to the classification task, which contributes to formulating the smoothness cost. The cost function is optimized using graph cuts to get the final segmentation of the prostate. With average dice metric (DM) (on the training set) and DM (on the test set), our experimental results show that inclusion of the context and semantic information contributes to higher segmentation accuracy than other methods.