Label-Independent Information Compression for Skin Diseases Recognition
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0065-3_14
Reference32 articles.
1. Al-masni, M.A., Kim, D.H., Kim, T.S.: Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput. Methods Programs Biomed. 190, 105351 (2020). https://doi.org/10.1016/j.cmpb.2020.105351
2. Ba, W., et al.: Convolutional neural network assistance significantly improves dermatologists’ diagnosis of cutaneous tumours using clinical images. Eur. J. Cancer 169, 156–165 (2022). https://doi.org/10.1016/j.ejca.2022.04.015
3. Balch, C.M., et al.: Final version of 2009 ajcc melanoma staging and classification. J. Clin. Oncol. 27, 6199 (2009)
4. Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (isic) (2019)
5. El Saleh, R., Bakhshi, S., Amine, N.A.: Deep convolutional neural network for face skin diseases identification. In: 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), pp. 1–4. IEEE (2019)
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