We present an interpretable end-to-end computer-aided detection and diagnosis tool for pulmonary nodules on computed tomography (CT) using deep learning-based methods. The proposed network consists of a nodule detector and a nodule malignancy classifier. We used RetinaNet to train a nodule detector using 7,607 slices containing 4,234 nodule annotations and validated it using 2,323 slices containing 1,454 nodule annotations drawn from the LIDC-IDRI dataset. The average precision for the nodule class in the validation set reached 0.24 at an intersection over union (IoU) of 0.5. The trained nodule detector was externally validated using a UCLA dataset. We then used a hierarchical semantic convolutional neural network (HSCNN) to classify whether a nodule was benign or malignant and generate semantic (radiologist-interpretable) features (e.g., mean diameter, consistency, margin), training the model on 149 cases with diagnostic CTs collected from the same UCLA dataset. A total of 149 nodule-centered patches from the UCLA dataset were used to train the HSCNN. Using 5-fold cross validation and data augmentation, the mean AUC and mean accuracy in the validation set for predicting nodule malignancy achieved 0.89 and 0.74, respectively. Meanwhile, the mean accuracy for predicting nodule mean diameter, consistency, and margin were 0.59, 0.74, and 0.75, respectively. We have developed an initial end-to-end pipeline that automatically detects nodules ≥ 5 mm on CT studies and labels identified nodules with radiologist-interpreted features automatically.
We first discuss the ultimate specifications of an augmented reality display that would saturate the human perception. Thereafter our study identifies fundamental limitations and trade-offs enforced by laws of optics for any augmented reality display that uses passive optical elements such as visors, waveguides, and meta-surfaces to deliver the image to the eye. The limitations are categorized into 7 rules that optics designers must consider when they are designing augmented reality glasses. These rules are directly drawn from Fermat's principle, perturbation theory, linear optics reciprocity, and human visual perception principles. Based on psychophysical theories we further work toward defining and quantizing levels of depth that would saturate the human depth perception. Our results indicate that passive optics acts as a passive system with less than unity pulse response function that would always reduce the performance of the original light source. Additionally, our investigations reveal the dynamics between allocation of depth levels and number of depth levels for ultimate lighfield experiences.
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