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Content-based retrieval supports a radiologist decision making process bypresenting the doctor the most similar cases from the database containing bothhistorical diagnosis and further disease development history. We present a deeplearning system that transforms a 3D image of a pulmonary nodule from a CT scaninto a low-dimensional embedding vector. We demonstrate that such a vectorrepresentation preserves semantic information about the nodule and offers aviable approach for content-based image retrieval (CBIR). We discuss thetheoretical limitations of the available datasets and overcome them by applyingtransfer learning of the state-of-the-art lung nodule detection model. Weevaluate the system using the LIDC-IDRI dataset of thoracic CT scans. We devisea similarity score and show that it can be utilized to measure similarity 1)between annotations of the same nodule by different radiologists and 2) betweenthe query nodule and the top four CBIR results. A comparison between doctorsand algorithm scores suggests that the benefit provided by the system to theradiologist end-user is comparable to obtaining a second radiologist's opinion.

Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation