Global and Local Explanations for Skin Cancer Diagnosis Using Prototypes

Author:

Santiago Carlos,Correia Miguel,Verdelho Maria Rita,Bissoto Alceu,Barata Catarina

Publisher

Springer Nature Switzerland

Reference20 articles.

1. Barata, C., et al.: Improving dermoscopy image classification using color constancy. IEEE JBHI 19, 1146–1152 (2015)

2. Chen, C., et al.: This looks like that: deep learning for interpretable image recognition. In: Advances in Neural Information Processing Systems 32 (2019)

3. Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp. 168–172 (2018)

4. Combalia, M., et al.: Bcn20000: dermoscopic lesions in the wild. arXiv preprint arXiv:1908.02288 (2019)

5. Daneshjou, R., et al.: Checklist for evaluation of image-based artificial intelligence reports in dermatology: clear derm consensus guidelines from the international skin imaging collaboration artificial intelligence working group. JAMA Dermatol. 158(1), 90–96 (2022)

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