Learning from Experts: Developing Transferable Deep Features for Patient Level Lung Cancer Prediction
Due to recent progress in Convolutional Neural Networks (CNNs) developing image based CNN models for predictive diagnosis is gaining enormous interest. However, to date, insufficient imaging samples with truly pathological proven labels impede the evaluation of CNN models at scale. In this paper, we formulate a domain adaptation framework that learns transferable deep features for patient level lung cancer malignancy prediction. The presented work learns CNN based features from a large discovery set (2272 lung nodules) with malignancy likelihood labels involving multiple radiologists' assessments, and then tests the transferable predictability of these CNN based features on a diagnosis definite set (115 cases) with true pathologically proven lung cancer labels. We evaluate our approach on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC IDRI) dataset, where both human expert labeling information on cancer malignancy likelihood and a set of pathologically proven malignancy labels were provided. Experimental results demonstrate the superior predictive performance of the transferable deep features on predicting true patient level lung cancer malignancy (Acc 70.69 AUC 0.66) which outperforms a nodule level CNN model (Acc 65.38 AUC 0.63) and is even comparable to that of using the radiologists' knowledge (Acc 72.41 AUC 0.76) The proposed model can largely reduce the demand for pathologically proven data, holding promise to empower cancer diagnosis by leveraging multi source CT imaging datasets.
MICCAI