A probabilistic approach for interpretable deep learning in liver cancer diagnosis

Abstract

Despite rapid advances in deep learning applications for radiological diagnosis and prognosis, the clinical adoption of such models is limited by their inability to explain or justify their predictions. This work developed a probabilistic approach for interpreting the predictions of a convolutional neural network (CNN) trained to classify liver lesions from multiphase magnetic resonance imaging (MRI). It determined the presence of 14 radiological features, where each lesion image contained one to four features and only ten examples of each feature were provided. Using stochastic forward passes of these example images through a trained CNN, samples were obtained from each feature's conditional probability distribution over the network's intermediate outputs. The marginal distribution was sampled with stochastic forward passes of images from the entire training dataset, and sparse kernel density estimation (KDE) was used to infer which features were present in a test set of 60 lesion images. This approach was tested on a CNN that reached 89.7% accuracy in classifying six types of liver lesions. It identified radiological features with 72.2 ± 2.2% precision and 82.6 ± 2.0% recall. In contrast with previous interpretability approaches, this method used sparsely labeled data, did not change the CNN architecture, and directly outputted radiological descriptors of each image. This approach can identify and explain potential failure modes in a CNN, as well as make a CNN's predictions more transparent to radiologists. Such contributions could facilitate the clinical translation of deep learning in a wide range of diagnostic and prognostic applications.