The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Open accessAbstract
Despite the recent advances in automatically describing image contents, their applications have been mostly limited to image caption datasets containing natural images (e.g., Flickr 30k, MSCOCO). In this paper, we present a deep learning model to efficiently detect a disease from an image and annotate its contexts (e.g., location, severity and the affected organs). We employ a publicly available radiology dataset of chest x-rays and their reports, and use its image annotations to mine disease names to train convolutional neural networks (CNNs). In doing so, we adopt various regularization techniques to circumvent the large normalvs-diseased cases bias. Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features. Moreover, we introduce a novel approach to use the weights of the already trained pair of CNN/RNN on the domain-specific image/text dataset, to infer the joint image/text contexts for composite image labeling. Significantly improved image annotation results are demonstrated using the recurrent neural cascade model by taking the joint image/text contexts into account.
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Cites
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Cited by
A survey on deep learning in medical image analysis
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Self-citationShifting machine learning for healthcare from development to deployment and from models to data
Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00898-y · Cited by 420
On the Automatic Generation of Medical Imaging Reports
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Metadata
- Authors
- Shin, H.-C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., & Summers, R. M.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 2497-2506
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2334763311
- Cited by
- 373
- References
- 64
- FWCI
- 13.16
- Type
- conference paper
- Language
- en
- Open access
- green
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- Shin, H.-C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., & Summers, R. M. (2016). Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.274
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