B-0912 - Opening the "Black Box": radiological insights into a deep neural network for lung nodule characterisation

Description

Abstract Presentation Number: B-0912

Purpose: To explain predictions of a deep residual convolutional network for characterization of lung nodule by analysing heat maps.

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Methods and Materials: A 20-layer deep residual CNN was trained on 1245 chest CTs from NLST trial to predict the malignancy risk of a nodule. We used occlusion to systematically block regions of a nodule and map drops in malignancy risk score to generate clinical attribution heatmaps on 160 nodules from LIDC-IDRI dataset, which were analysed by a thoracic radiologist. The features were described as heat inside nodule (IH) - bright areas inside nodule, peripheral heat (PH) - continuous/interrupted bright areas along nodule contours, heat in adjacent plane(AH) - brightness in scan planes juxtaposed with the nodule, satellite heat (SH) - a smaller bright spot in proximity to nodule in the same scan plane, heat map larger than nodule (LH) - bright areas corresponding to the shape of the nodule seen outside the nodule margins and heat in calcification (CH).

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Results: These six features were assigned binary values. This feature vector was fed into a standard J48 decision tree with tenfold cross-validation, which gave an 85% weighted classification accuracy with a 77.8%TP rate, 8% FP rate for benign cases and 91.8% TP and 22.2%FP rates for malignant cases. IH was more frequently observed in nodules classified as malignant whereas PH, AH, and SH were more commonly seen in nodules classified as benign.

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Conclusion: We discuss the potential ability of a radiologist to visually parse the deep learning algorithm-generated 'heat map' to identify features aiding classification.

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