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Localization of Critical Findings in Chest X-Ray Without Local Annotations Using Multi-Instance Learning
Open accessAbstract
The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings. While deep learning (DL) models has become a promising predictive technology with near-human accuracy, they commonly suffer from a lack of explainability, which is an important aspect for clinical deployment of DL models in the highly regulated healthcare industry. For example, localizing critical findings in an image is useful for explaining the predictions of DL classification algorithms. While there have been a host of joint classification and localization methods for computer vision, the state-of-the-art DL models require locally annotated training data in the form of pixel level labels or bounding box coordinates. In the medical domain, this requires an expensive amount of manual annotation by medical experts for each critical finding. This requirement becomes a major barrier for training models that can rapidly scale to various findings. In this work, we address these shortcomings with an interpretable DL algorithm based on multi-instance learning that jointly classifies and localizes critical findings in CXR without the need for local annotations. We show competitive classification results on three different critical findings (pneumothorax, pneumonia, and pulmonary edema) from three different CXR datasets.
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“Schwab et al. (2020) localized critical findings in chest X-ray using such a patch-based approach.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Schwab et al. (2020)
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“Each image patch received a prediction, and the predictions were overlaid on the image to visualize on which areas the classifier based its decision.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Schwab et al. (2020)
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep LearningPreprint
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D. Y., Bagul, A., Langlotz, C. P., Shpanskaya, K. S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05225 · Cited by 4,358
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A. E. W., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0322-0 · Cited by 1,816
Thorax disease classification with attention guided convolutional neural network
Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., & Yang, Y. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.040 · Cited by 456
Classifying and segmenting microscopy images with deep multiple instance learning
Kraus, O. Z., Ba, J. L., & Frey, B. J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw252 · Cited by 411
Deep learning of feature representation with multiple instance learning for medical image analysis
Xu, Y. L., Mo, T., Feng, Q., Zhong, P., Lai, M., & Chang, E. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853873 · Cited by 390
Thoracic Disease Identification and Localization with Limited Supervision
Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.-J., & Fei-Fei, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00865 · Cited by 352
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs
Johnson, A. E. W., Pollard, T. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Peng, Y., Lu, Z., Mark, R. G., Berkowitz, S. J., & Horng, S. (2019) · arXiv · doi:10.48550/arxiv.1901.07042 · Cited by 269
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Zhu, W., Lou, Q., Vang, Y. S., & Xie, X. S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_69 · Cited by 256
Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., & Yang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.09927 · Cited by 235
Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition
Yan, Z., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., Metaxas, D., & Zhou, X. S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2524985 · Cited by 227
MIMIC-CXR: A large publicly available database of labeled chest radiographsPreprint
Johnson, A. E. W., Pollard, T., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · arXiv (Cornell University) · Cited by 182
Actionable Findings and the Role of IT Support: Report of the ACR Actionable Reporting Work Group
Larson, P. A., Berland, L. L., Griffith, B. D., Kahn, C. E., & Liebscher, L. A. (2014) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2013.12.016 · Cited by 110
Gooßen, A., Deshpande, H., Harder, T., Schwab, E., Baltruschat, I. M., Mabotuwana, T., Cross, N. M., & Saalbach, A. (2019) · Cited by 6
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Cited by
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328
Deep learning for chest X-ray analysis: A survey
Çallı, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K. G., & Murphy, K. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102125 · Cited by 572
Elyan, E., Vuttipittayamongkol, P., Johnston, P., Martin, K., McPherson, K., Moreno‐García, C. F., Jayne, C., & Sarker, M. M. K. (2022) · Artificial Intelligence Surgery · doi:10.20517/ais.2021.15 · Cited by 182
Artificial intelligence surgery: how do we get to autonomous actions in surgery?
Gumbs, A. A., Frigerio, I., Spolverato, G., Croner, R., Illanes, A., Chouillard, E., & Elyan, E. (2021) · Sensors · doi:10.3390/s21165526 · Cited by 161
Houssein, E. H., Gamal, A. M., Younis, E. M. G., & Mohamed, E. (2025) · Cluster Computing · doi:10.1007/s10586-025-05281-5 · Cited by 153
Metadata
- Authors
- Schwab, E., Goossen, A., Deshpande, H., & Saalbach, A.
- Affiliations
- Philips (United States) (US)
- Philips (Germany) (DE)
- Venue
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2020-04
- Pages
- 1879-1882
- OpenAlex
- W3001055052
- Cited by
- 24
- References
- 14
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 7 · 2022: 4 · 2021: 6 · 2020: 1
- FWCI
- 4.88
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE RESOURCE CENTERS
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- chest radiography, multiple instance learning, explainable deep learning, pneumothorax detection, pneumonia detection, annotation-free training, medical image interpretation
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