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A Localisation Study of Deep Learning Models for Chest X-ray Image Classification

Gascoigne-Burns, James; Katsigiannis, Stamos

A Localisation Study of Deep Learning Models for Chest X-ray Image Classification Thumbnail


James Gascoigne-Burns


Deep learning models have demonstrated superhuman performance in a multitude of image classification tasks, including the classification of chest X-ray images. Despite this, medical professionals are reluctant to embrace these models in clinical settings due to a lack of interpretability, citing being able to visualise the image areas contributing most to a model's predictions as one of the best ways to establish trust. To aid the discussion of their suitability for real-world use, in this work, we attempt to address this issue by conducting a localisation study of two state-of-the-art deep learning models for chest X-ray image classification, ResNet-38-large-meta and CheXNet, on a set of 984 radiologist annotated X-ray images from the publicly available ChestX-ray14 dataset. We do this by applying and comparing several state-of-the-art visualisation methods, combined with a novel dynamic thresholding approach for generating bounding boxes, which we show to outperform the static thresholding method used by similar localisation studies in the literature. Results also seem to indicate that localisation quality is more sensitive to the choice of thresholding scheme than the visualisation method used, and that a high discriminative ability as measured by classification performance is not necessarily sufficient for models to produce useful and accurate localisations.


Gascoigne-Burns, J., & Katsigiannis, S. (2022). A Localisation Study of Deep Learning Models for Chest X-ray Image Classification. .

Conference Name 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
Conference Location Ioannina, Greece
Start Date Sep 27, 2022
End Date Sep 30, 2022
Acceptance Date Jul 19, 2022
Online Publication Date Nov 4, 2022
Publication Date 2022
Deposit Date Jul 21, 2022
Publicly Available Date Apr 28, 2023
Series ISSN 2641-3590,2641-3604


Accepted Conference Proceeding (1.2 Mb)

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