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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 endto-end pipeline that automatically detects nodules ≥ 5 mm on CT studies and labels identified nodules with radiologist-interpreted features automatically.

PCB-PDN design remains a challenge with the reducing noise margins. One aspect of PDN design is finding the number of decoupling capacitors required for each power rail. As more capacitors are added, the mid frequency equivalent inductance in the impedance of the PCB-PDN converges to a minimum value for each placement pattern. This convergence is studied for different placement patterns to find the least number of capacitors required to satisfy a certain convergence criteria. A first principle method is used resonant cavity model for the analysis.