Carlo Metta

Carlo Metta

Involved in the research line 1 ▪ 2 ▪ 3 ▪4

Role: Researcher

Affiliation: ISTI - CNR Pisa


9.

[MBG2023]
Improving trust and confidence in medical skin lesion diagnosis through explainable deep learning
Carlo Metta, Andrea Beretta, Riccardo Guidotti, Yuan Yin, Patrick Gallinari, Salvatore Rinzivillo, Fosca Giannotti (2023) - Springer Nature. In International Journal of Data Science and Analytics

Abstract

A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we aim at improving the trust and confidence of users towards automatic AI decision systems in the field of medical skin lesion diagnosis by customizing an existing XAI approach for explaining an AI model able to recognize different types of skin lesions. The explanation is generated through the use of synthetic exemplar and counter-exemplar images of skin lesions and our contribution offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A validation survey with domain experts, beginners, and unskilled people shows that the use of explanations improves trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon may stem from the intrinsic characteristics of each class and may help resolve common misclassifications made by human experts.

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Research Line 1▪3

33.

[MGY2021]
Exemplars and Counterexemplars Explanations for Skin Lesion Classifiers
Carlo Metta, Riccardo Guidotti, Yuan Yin, Patrick Gallinari, Salvatore Rinzivillo (2021) - IOS Press. In HHAI2022: Augmenting Human Intellect, S. Schlobach et al. (Eds.)

Abstract

Explainable AI consists in developing models allowing interaction between decision systems and humans by making the decisions understandable. We propose a case study for skin lesion diagnosis showing how it is possible to provide explanations of the decisions of deep neural network trained to label skin lesions.

39.

[MGY2021]
Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling
Metta Carlo, Guidotti Riccardo, Yin Yuan, Gallinari Patrick, Rinzivillo Salvatore (2021) - 2021 IEEE Symposium on Computers and Communications (ISCC). In 2021 IEEE Symposium on Computers and Communications (ISCC)

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.

41.

[MBG2021]
Explainable Deep Image Classifiers for Skin Lesion Diagnosis
Metta Carlo, Beretta Andrea, Guidotti Riccardo, Yin Yuan, Gallinari Patrick, Rinzivillo Salvatore, Giannotti Fosca (2021) - Arxive preprint. In International Journal of Data Science and Analytics

Abstract

A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems. Research in eXplainable Artificial Intelligence (XAI) is trying to solve this issue. However, often XAI approaches are only tested on generalist classifier and do not represent realistic problems such as those of medical diagnosis. In this paper, we analyze a case study on skin lesion images where we customize an existing XAI approach for explaining a deep learning model able to recognize different types of skin lesions. The explanation is formed by synthetic exemplar and counter-exemplar images of skin lesion and offers the practitioner a way to highlight the crucial traits responsible for the classification decision. A survey conducted with domain experts, beginners and unskilled people proof that the usage of explanations increases the trust and confidence in the automatic decision system. Also, an analysis of the latent space adopted by the explainer unveils that some of the most frequent skin lesion classes are distinctly separated. This phenomenon could derive from the intrinsic characteristics of each class and, hopefully, can provide support in the resolution of the most frequent misclassifications by human experts.

More information

External Link

Research Line 1▪3▪4