Improving the Fairness of Chest X|ray Classifiers

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

Title:Improving the Fairness of Chest X-ray Classifiers ... Abstract:Deep learning models have reached or surpassed human-level performance in the ...

Improving the Fairness of Chest X-ray Classifiers

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-.

Improving the Fairness of Chest X-ray Classifiers - GitHub

Improving the Fairness of Chest X-ray Classifiers. Contribute to MLforHealth/CXR_Fairness development by creating an account on GitHub.

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

Haoran is a fourth year PhD student in EECS at MIT. He is generally interested in building robust machine learning models that maintain their performance and ...

Improving Fairness of Automated Chest Radiograph Diagnosis by ...

Employing SCL can mitigate bias in chest radiograph diagnosis, addressing concerns of fairness and reliability in deep learning–based diagnostic ...

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

Request PDF | Improving the Fairness of Chest X-ray Classifiers | Deep learning models have reached or surpassed human-level performance in ...

Generative models improve fairness of medical classifiers under ...

Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be ...

NeurIPS Improving the Fairness of Deep Chest X-ray Classifiers

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Improving Fairness of Automated Chest X-ray Diagnosis by ...

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

Generative models improve fairness of medical classifiers ... - arXiv

This approach leads to stark improvements OOD across modalities: 7.7% prediction accuracy improvement in histopathology, 5.2% in chest radiology ...

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

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

CheXclusion: Fairness Gaps in Deep Chest X-ray Classifiers

Fairness in machine learning models is a topic of increasing at- tention, spanning sex bias in occupation classifiers,16 racial bias in criminal defendant risk.

Generative models improve fairness of medical classifiers under ...

One of the first studies to dive into the effect of training data composition on model performance across the sexes when using chest X-rays to ...

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

Prior work has demonstrated the surprising ability of deep learning models to recover demographic information from chest X-rays. This suggests ...

Acquisition parameters influence AI recognition of race in chest x ...

Using two popular chest x-ray datasets, we first demonstrate that technical parameters related to image acquisition and processing influence AI ...

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.

Fairness and Explainability in Chest X-ray Image Classifiers

In order to increase the power of our analysis, we explored alternative evaluation methods, like deletion or insertion curves, but reported them as ...

Risk of Bias in Chest Radiography Deep Learning Foundation Models

... classification submodels with increasing complexity. The classification ... CheXclusion: Fairness gaps in deep chest X-ray classifiers . Pac Symp ...

Auditing Fairness and Explainability in Chest X-Ray Image Classifiers

Since our goal is not to improve the state-of-the art of medical image classifiers, we re- implemented models from a previous study (Seyyed-. Kalantari et al ...

CheXclusion: Fairness gaps in deep chest X-ray classifiers

CheXclusion: Fairness gaps in deep chest X-ray classifiers ... Abstract: Machine learning systems have received much attention recently for their ability to ...