Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

Joana Rocha,Ana Maria Mendonça ,Aurélio Campilho

doi:10.24840/2183-6493_007.004_0002

Abstract

Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.

Highlights

Among the popular medical imaging exams, the Chest X-Ray (CXR) is frequently requested by healthcare professionals to assess the presence of thoracic diseases, due to its low-cost noninvasive nature
Computer-aided diagnosis seeks toprovide a second opinion to healthcare professionals, reducing their workload and promoting a more accurate early diagnosis. These systems are important to analyse CXR images containing complex information on a variety of pathologies that affect vital organs
Recent advances in Deep Learning (DL) strategies and computational resources have led to a steep performance increase in CXR-based computer-aided diagnosis algorithms, which escalated due to the availability of larger annotated public CXR datasets

Summary

Introduction

Among the popular medical imaging exams, the Chest X-Ray (CXR) is frequently requested by healthcare professionals to assess the presence of thoracic diseases, due to its low-cost noninvasive nature. The National Institutes of Health (NIH) released the ChestX-ray in Wang et al (2017a) and compiled 108 948 frontal views belonging to 32 717 unique patients and a total of 8 associated pathologies (Figure 1) extracted from radiological reports using natural language processing This dataset evolved to include 6 more categories, increasing the overall number of frontal CXR images and resulting in ChestX-ray. PadChest became very recently available in Bustos et al (2020), containing a total of 193 labels applied to 160 868 frontal and lateral CXR images of 67 625 patients It was collected from the San Juan Hospital, considering radiology reports written in Spanish. Infi l tra ti on Ma s s Nodul e Emphys ema Fi bros i s Pleural thickening Herni a

Abnormality Detection

Multi-label Thoracic Pathology Classification

Conclusions