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- W4200502028 abstract "Explainable AI consists in developing mechanisms allowing for an interaction between decision systems and humans by making the decisions of the formers understandable. This is particularly important in sensitive contexts like in the medical domain. We propose a use case study, for skin lesion diagnosis, illustrating how it is possible to provide the practitioner with explanations on the decisions of a state of the art deep neural network classifier trained to characterize skin lesions from examples. Our framework consists of a trained classifier onto which an explanation module operates. The latter is able to offer the practitioner exemplars and counterexemplars for the classification diagnosis thus allowing the physician to interact with the automatic diagnosis system. The exemplars are generated via an adversarial autoencoder. We illustrate the behavior of the system on representative examples." @default.
- W4200502028 created "2021-12-31" @default.
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- W4200502028 date "2021-09-05" @default.
- W4200502028 modified "2023-09-30" @default.
- W4200502028 title "Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling" @default.
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- W4200502028 doi "https://doi.org/10.1109/iscc53001.2021.9631485" @default.
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