Share Email Print
cover

Proceedings Paper

EDICNet: An end-to-end detection and interpretable malignancy classification network for pulmonary nodules in computed tomography
Author(s): Yannan Lin; Leihao Wei; Simon X. Han; Denise R. Aberle; William Hsu
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 12 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141H (16 March 2020);
Show Author Affiliations
Yannan Lin, Univ. of California, Los Angeles (United States)
Leihao Wei, Univ. of California, Los Angeles (United States)
Simon X. Han, Univ. of California, Los Angeles (United States)
Denise R. Aberle, Univ. of California, Los Angeles (United States)
William Hsu, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray