Deep regression via multi-channel multi-modal learning for pneumonia screening

Abstract

Pneumonia screening is one of the most crucial steps in the pneumonia diagnosing system, which can improve the work efficiency of the radiologists and prevent delayed treatments. In this paper, we propose a deep regression framework for automatic pneumonia screening, which jointly learns the multi-channel images and multi-modal information (i.e., clinical chief complaints, age, and gender) to simulate the clinical pneumonia screening process. We demonstrate the advantages of the framework in three ways. First, visual features from multi-channel images (Lung Window Images, High Attenuation Images, Low Attenuation Images) can provide more visual features than single image channel, and improve the ability of screening pneumonia with severe diseases. Second, the proposed framework treats chest CT scans as short video frames and analyzes them by using Recurrent Convolutional Neural Network, which can automatically extract multiple image features from multi-channel image slices. Third, chief complaints and demographic information can provide valuable prior knowledge enhancing the features from images and further promote performance. The proposed framework has been extensively validated in 900 clinical cases. Compared to the baseline, the proposed framework improves the accuracy by 2.3% and significantly improves the sensitivity by 3.1%. To the best of our knowledge, we are the first to screen pneumonia using multi-channel images, multi-modal demographic and clinical information based on the large scale clinical raw dataset.