NeurIPS Improving the Fairness of Deep Chest X|ray Classifiers

NeurIPS Improving the Fairness of Deep Chest X-ray Classifiers

The NeurIPS Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale.

[2203.12609] Improving the Fairness of Chest X-ray Classifiers - arXiv

Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using ...

Improving the Fairness of Chest X-ray Classifiers

Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using chest x-rays.

On Mitigating Shortcut Learning for Fair Chest X-ray Classification ...

The NeurIPS Logo above may be used on presentations. Right-click and choose download. It is a vector graphic and may be used at any scale. Useful links ...

Publications | Healthy ML

CheXclusion: Fairness gaps in deep chest X-ray classifiers. Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y Chen, Marzyeh Ghassemi. 2020 ...

Algorithmic Fairness in Chest X-ray Diagnosis: A Case Study

In this case study, we use several distinct concepts of algorithmic fairness to analyze a deep learning model that predicts from their chest X-ray whether ...

Medical Imaging meets NeurIPS

Improving the Fairness of Deep Chest X-ray Classifiers ( Poster ) >. Haoran Zhang · Natalie Dullerud · Karsten Roth · Stephen Pfohl · Marzyeh ...

Addressing fairness issues in deep learning-based medical image ...

Improving the fairness of chest x-ray classifiers. In Conference on Health, Inference, and Learning 204–233 (PMLR, 2022). Ganz, M., Holm ...

Medical AI fairness resources

Zhang et al (2022), Improving the Fairness of Chest X-ray Classifiers, CHIL. ... Glocker et al (2023), Risk of Bias in Chest Radiography Deep Learning Foundation ...

A collection of papers in fairness of medical image analysis - GitHub

Fairness Benchmarking · Improving the Fairness of Chest X-ray Classifiers. PCHIL, 2022. (paper) · Medfair: Benchmarking fairness for medical imaging. ICLR, 2023.

Medical Imaging meets NeurIPS

On Mitigating Shortcut Learning for Fair Chest X-ray Classification ( Poster ) > ... MIMIC-NLE-v2: Can Large Language Models Reason about Chest X-rays?

The limits of fair medical imaging AI in real-world generalization

CheXclusion: Fairness Gaps in Deep Chest X-ray Classifiers. ... Improving the fairness of chest x-ray classifiers. In Proc. of the ...

Drop the shortcuts: image augmentation improves fairness and ...

∙ et al. Comparison of deep learning approaches for multi-label chest X-ray classification. Sci Rep. 2019; 9:6381. Crossref.

Improving the Fairness of Chest X-ray Classifiers | Request PDF

Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis ...

image augmentation improves fairness and decreases AI detection ...

2. Baltruschat IM, Nickisch H, Grass M, Knopp T, Saalbach A. Comparison of deep learning approaches for multi-label chest X-ray classification.

Algorithm fairness in artificial intelligence for medicine and healthcare

... fairness gaps in deep chest X-ray classifiers. In BIOCOMPUTING 2021 ... Federated learning improves site performance in multicenter deep learning ...

The limits of fair medical imaging AI in real-world generalization

CheXclusion: Fairness Gaps in Deep Chest X-ray Classifiers. Pacific Symposium on ... Improving the fairness of chest x-ray classifiers. In Proc. of the ...

“Shortcuts” Causing Bias in Radiology Artificial Intelligence

Open-source Python + R, Access to fairness-enhancing classification algorithms ... CheXclusion: fairness gaps in deep chest x-ray classifiers. Pac Symp ...

Evaluation and Mitigation of Racial Bias in Clinical Machine ...

Crucially, improving model fairness does not necessarily ... CheXclusion: fairness gaps in deep chest X-ray classifiers. Pacific ...

Improving the Fairness of Chest X-ray Classifiers - Healthy ML

from McMaster University. Related. An empirical framework for domain generalization in clinical settings · Is Fairness Only Metric Deep?