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Purpose Automated anatomical labeling facilitates the diagnostic process for physicians and radiologists. One of the challenges in automated anatomical labeling problems is the robustness to handle the large individual variability inherited in human anatomy. A novel deep neural network framework, referred to Tree Labeling Network (TreeLab-Net), is proposed to resolve this problem in this work. Methods...
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization...
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