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arXiv · 2012.12712

Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures

Candelaria Mosquera·Facundo Nahuel Diaz·Fernando Binder·Jose Martin Rabellino·Sonia Elizabeth Benitez·Alejandro Daniel Beresñak·Alberto Seehaus·Gabriel Ducrey·Jorge Alberto Ocantos·Daniel Roberto Luna
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Abstract

BACKGROUND AND OBJECTIVES: The multiple chest x-ray datasets released in the last years have ground-truth labels intended for different computer vision tasks, suggesting that performance in automated chest-xray interpretation might improve by using a method that can exploit diverse types of annotations. This work presents a Deep Learning method based on the late fusion of different convolutional architectures, that allows training with heterogeneous data with a simple implementation, and evaluates its performance on independent test data. We focused on obtaining a clinically useful tool that could be successfully integrated into a hospital workflow. MATERIALS AND METHODS: Based on expert opinion, we selected four target chest x-ray findings, namely lung opacities, fractures, pneumothorax and pleural effusion. For each finding we defined the most adequate type of ground-truth label, and built four training datasets combining images from public chest x-ray datasets and our institutional archive. We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool. Performance was measured on two test datasets: an external openly-available dataset, and a retrospective institutional dataset, to estimate performance on local population. RESULTS: The external and local test sets had 4376 and 1064 images, respectively, for which the model showed an area under the Receiver Operating Characteristics curve of 0.75 (95%CI: 0.74-0.76) and 0.87 (95%CI: 0.86-0.89) in the detection of abnormal chest x-rays. For the local population, a sensitivity of 86% (95%CI: 84-90), and a specificity of 88% (95%CI: 86-90) were obtained, with no significant differences between demographic subgroups. We present examples of heatmaps to show the accomplished level of interpretability, examining true and false positives.

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Candelaria Mosquera, Facundo Nahuel Diaz, Fernando Binder, Jose Martin Rabellino, Sonia Elizabeth Benitez, Alejandro Daniel Beresñak, Alberto Seehaus, Gabriel Ducrey, Jorge Alberto Ocantos, Daniel Roberto Luna. 2020-12-23. Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures. https://arxiv.org/abs/2012.12712

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