Chest pathology detection using deep learning with non-medical training
Abstract
In this work, we examine the strength of deep learning approaches for pathology detection in chest radiographs. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of CNN learned from a non-medical dataset to identify different types of pathologies in chest x-rays. We tested our algorithm on a 433 image dataset. The best performance was achieved using CNN and GIST features. We obtained an area under curve (AUC) of 0.87-0.94 for the different pathologies. The results demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. This is a first-of-its-kind experiment that shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.
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To complement off-the-shelf CNN features, [10], [9], [12] all add and integrate various other hand-crafted image features as hybrid inputs for final CADe classification.
“To complement “off-the-shelf” CNN features, [10], [9], [12] all add and integrate various other hand-crafted image features as hybrid inputs for the final CADe classification.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [10]
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Weakly Supervised Prostate Tma Classification Via Graph Convolutional Networks
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Classification of breast lesions using cross-modal deep learning
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Medinoid: Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning †
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- Authors
- Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H.
- Affiliations
- Tel Aviv University (IL)
- Sheba Medical Center (IL)
- Venue
- 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2015-04
- Pages
- 294-297
- OpenAlex
- W1570613334
- Cited by
- 392
- References
- 16
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 18 · 2023: 31 · 2022: 38 · 2021: 59 · 2020: 69 · 2019: 55 · 2018: 45 · 2017: 41 · 2016: 19 · 2015: 3
- FWCI
- 87.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ImageNet pre-training, transfer learning, convolutional neural networks, GIST features, medical image classification, domain adaptation
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