Synthetic Data as a Tool to Combat Racial Bias in Medical AI: Utilizing Generative Models for Optimizing Early Detection of Melanoma in Fitzpatrick Skin Types IV–VI

KVAK, Daniel; Eva BŘEZINOVÁ; Marek BIROŠ a Robert HRUBÝ. Synthetic Data as a Tool to Combat Racial Bias in Medical AI: Utilizing Generative Models for Optimizing Early Detection of Melanoma in Fitzpatrick Skin Types IV–VI. In 3rd International Conference on Medical Image and Computer-Aided Diagnosis (MICAD 2022). 2022.

Základní údaje

Originální název

Synthetic Data as a Tool to Combat Racial Bias in Medical AI: Utilizing Generative Models for Optimizing Early Detection of Melanoma in Fitzpatrick Skin Types IV–VI

Název anglicky

Synthetic Data as a Tool to Combat Racial Bias in Medical AI: Utilizing Generative Models for Optimizing Early Detection of Melanoma in Fitzpatrick Skin Types IV–VI

Autoři

KVAK, Daniel; Eva BŘEZINOVÁ; Marek BIROŠ a Robert HRUBÝ

Vydání

3rd International Conference on Medical Image and Computer-Aided Diagnosis (MICAD 2022), 2022

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Další údaje

Typ výsledku

Konferenční abstrakt

Obor

30216 Dermatology and venereal diseases

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Označené pro přenos do RIV

Ne

Klíčová slova anglicky

Autoregressive Models, Computer-Aided Diagnosis, Deep Learning, Generative Adversarial Networks, Melanoma, Synthetic Data, Zero-Shot Learn-ing.

Příznaky

Mezinárodní význam, Recenzováno

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Anotace

V originále

Assistive tools to aid in skin cancer detection are experiencing an unprecedented rise with the accessibility of robust and accurate deep learning models. However, in the present applications, only a negligible number of dermatology images come from patients with Fitzpatrick skin types IV–VI, representing brown, dark brown or black skin, respectively. In this study, we demonstrate the utilization of Zero-Shot Text-to-Image autoregressive models to generate synthetic medical data for improved balance in training CAD classification models with minimized racial bias. Synthetically generated images of skin lesions were assessed by an experienced dermatologist using the ABCD rule and differential diagnostics, and subsequently validated using a pre-trained ResNet50V2 multi-class classification model.

Anglicky

Assistive tools to aid in skin cancer detection are experiencing an unprecedented rise with the accessibility of robust and accurate deep learning models. However, in the present applications, only a negligible number of dermatology images come from patients with Fitzpatrick skin types IV–VI, representing brown, dark brown or black skin, respectively. In this study, we demonstrate the utilization of Zero-Shot Text-to-Image autoregressive models to generate synthetic medical data for improved balance in training CAD classification models with minimized racial bias. Synthetically generated images of skin lesions were assessed by an experienced dermatologist using the ABCD rule and differential diagnostics, and subsequently validated using a pre-trained ResNet50V2 multi-class classification model.