Liver Lesion Localization using Deep Convolutional Neural Networks

Within the field of medicine, automated annotation of Computed Tomography (CT) scans is poised to revolutionize the practice of radiology and to improve patient care through increased efficiency and accuracy of diagnosis. In this work, we develop a deep learning algorithm designed for the automated detection of liver lesions on CT scans. We explore three distinct neural network architectures for the object detection task, with increasing complexity respectively. These consist of a baseline convolutional network, a transfer learning approach which incorporates VGG-16 features into the baseline model, and finally the Faster R-CNN network. We find that Faster R-CNN greatly outperforms the baseline and can achieve a sensitivity of 0.5 with one average false positive per image even when trained and evaluated on noisy labels.