The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Thoracic Disease Identification and Localization with Limited Supervision
Abstract
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate prediction model for these tasks usually requires a large number of images manually annotated with labels and finding sites of abnormalities. In reality, however, such annotated data are expensive to acquire, especially the ones with location annotations. We need methods that can work well with only a small amount of location annotations. To address this challenge, we present a unified approach that simultaneously performs disease identification and localization through the same underlying model for all images. We demonstrate that our approach can effectively leverage both class information as well as limited location annotation, and significantly outperforms the comparative reference baseline in both classification and localization tasks.
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Self-citationYou Only Look Once: Unified, Real-Time Object Detection
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(2016) · Cited by 31,590
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Pneumonia Detection Using CNN based Feature Extraction
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A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection
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Self-citationRevisiting Reverse Distillation for Anomaly Detection
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Metadata
- Authors
- Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.-J., & Fei-Fei, L.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 8290-8299
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962708065
- Cited by
- 313
- References
- 38
- FWCI
- 79.13
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.-J., & Fei-Fei, L. (2018). Thoracic Disease Identification and Localization with Limited Supervision. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00865
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