Attention Aware Deep Learning Model for Wireless Capsule Endoscopy Lesion Classification and Localization
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s40846-022-00686-8.pdf
Reference49 articles.
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2. Ahn, J., Loc, H. N., Balan, R. K., Lee, Y., & Ko, J. (2018). Finding small-bowel lesions: Challenges in endoscopy-image-based learning systems. Computer, 51(5), 68–76. https://doi.org/10.1109/MC.2018.2381116
3. Guo, X., & Yuan, Y. (2020). Semi-supervised WCE image classification with adaptive aggregated attention. Medical Image Analysis, 64, 101733. https://doi.org/10.1016/j.media.2020.101733
4. Charfi, S., & El Ansari, M. (2020). A locally based feature descriptor for abnormalities detection. Soft Computing, 24(6), 4469–4481. https://doi.org/10.1007/s00500-019-04208-8
5. Ghosh, T., Fattah, S. A., & Wahid, K. A. (2018). CHOBS: Color histogram of block statistics for automatic bleeding detection in wireless capsule endoscopy video. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1–12. https://doi.org/10.1109/JTEHM.2017.2756034
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