Cross-Population Train/Test Deep Learning Model: Abnormality Screening in Chest X-Rays
Dipayan Das, KC Santosh, Umapada Pal
Abstract
Automated radiological screening is an advancing field in which algorithms and predictive models are used to detect abnormalities in Chest X-rays (CXRs). Traditionally, in machine learning, the exact same dataset has been partitioned into train and test sets, and as a consequence, the validation scores are often biased towards the population it has been trained on. Cross-population test is a measure of how good an algorithm/model performs after training on a data from one region of the world and then evaluating the model on another data from another part of the world, without any additional training or learning on the latter data. To showcase cross-population train/test model, we consider two benchmark CXR (with Tuberculosis) datasets that are made available by the U.S. National Library of Medicine: a) Shenzhen, China; and b) Montgomery County, USA. We used a modified pre-trained deep learning model as our predictive model and achieved a cross-population classification accuracy of 76.05% (0.84, AUC) and 71.47% (0.79, AUC), using each dataset as training and testing data separately. To the best of our knowledge, this is the first cross-population evaluation of a deep learning model being used for abnormality screening using CXRs.