EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography

Yannan Lin; Leihao Wei; Simon X. Han; Denise R. Aberle; William Hsu

doi:10.1117/12.2551220

16 March 2020 EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography

Yannan Lin, Leihao Wei, Simon X. Han, Denise R. Aberle, William Hsu

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113141H (2020) https://doi.org/10.1117/12.2551220
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

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.

Citation Download Citation

Yannan Lin, Leihao Wei, Simon X. Han, Denise R. Aberle, and William Hsu "EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141H (16 March 2020); https://doi.org/10.1117/12.2551220

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