Title: Adversarial debiasing with partial learning - medical image case-studies

Speaker: Ramon Correa

Abstract:
The use of artificial intelligence (AI) in healthcare has become a very active research area in the last few years. While significant progress has been made in image classification tasks, only a few AI methods are actually being deployed in hospitals. A major hurdle in actively using clinical AI models currently is the trustworthiness of these models. When scrutinized, these models reveal implicit biases during the decision making, such as detecting race, ethnic groups, and subpopulations. These biases result in poor model performance, or racial disparity, for patients in these minority groups. In our ongoing study, we develop a two-step adversarial debiasing approach with partial learning that can reduce the racial disparity while preserving the performance of the targeted task. The proposed methodology has been evaluated on two independent medical image case-studies - chest X-ray and mammograms, and showed promises in reducing racial disparity while preserving the performance.

Speaker Bio:
Ramon Correa is a Ph.D. student in ASU’s Data Science, Analytics, and Engineering program. His research interest involves studying model debiasing techniques. Previously, he completed his undergraduate studies at Case Western Reserve University, majoring in Biomedical Engineering.

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