Mar 23, 2022 · We benchmark the performance of nine methods in improving classifier fairness across these two definitions. We find, consistent with prior work ...

In this work, we aim to address these questions on the task of disease classification using chest x-ray images, focusing on group fairness and minimax fair-.

Benchmarking the performance of group-fair and minimax-fair methods on two chest x-ray datasets, with an auxiliary investigation into label bias in ...

Aug 21, 2024 · To develop an artificial intelligence model that uses supervised contrastive learning (SCL) to minimize bias in chest radiograph diagnosis.

He is generally interested in building robust machine learning models that maintain their performance and fairness across out-of-distribution environments, as ...

Sep 11, 2024 · We benchmark the performance of nine methods in improving classifier fairness across these two definitions. We find, consistent with prior work ...

Poster in. Workshop: Medical Imaging meets NeurIPS. Improving the Fairness of Deep Chest X-ray Classifiers. Haoran Zhang · Natalie Dullerud · Karsten Roth ...

Feb 27, 2023 · Here, we will evaluate classifiers along three dimensions: performance, calibration, and fairness. Machine learning models built for binary ...

Jan 25, 2024 · Our proposed method utilizes supervised contrastive learning with carefully selected positive and negative samples to generate fair image embeddings.

Conclusion: Employing supervised contrastive learning can mitigate bias in CXR diagnosis, addressing concerns of fairness and reliability in deep learning-based ...