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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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7 results for “CheXpert”

CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Outputcs.LG

CheXpert++ improves CheXpert's accuracy and usability for medical radiology reports.

problem Infeasibility of obtaining ground truth labels for medical data.

method BERT-based approximation of CheXpert, addressing speed, differentiability, and probabilistic output.

result Achieves 99.81% parity with CheXpert, significantly faster, differentiable, and probabilistic.

BERT medical radiology CheXpert differentiability probabilistic

2020-06-26abs ↗pdf ↗

Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classificationeess.IV

While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged. In medical imaging, there is a high heterogeneity of distributions among images based on the equipment that generates them and their parametrization. This heterogeneity trigger…

2019-09-03abs ↗pdf ↗

AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysiscs.LG

Paper detects biases in medical imaging ML models using counterfactual analysis.

problem Bias in medical imaging ML models negatively impacts generalization performance.

method Counterfactual invariance framework combining conditional latent diffusion models and statistical hypothesis testing.

result The method identifies and quantifies biases without direct access to counterfactual data.

medical imaging bias detection counterfactual analysis machine learning fairness

2025-04-28abs ↗pdf ↗

Post-hoc Orthogonalization for Mitigation of Protected Feature Bias in CXR Embeddingscs.LG

Study removes bias from chest X-ray embeddings using orthogonalization.

problem Reduces bias in chest X-ray embeddings due to protected features.

method Orthogonalization technique to remove protected feature effects.

result Orthogonalization removes bias and makes predictions of protected attributes infeasible.

chest x-ray deep learning bias mitigation protected features orthogonalization

2023-11-02abs ↗pdf ↗

CheXclusion: Fairness gaps in deep chest X-ray classifierscs.CV

Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we examine the extent to which state-of-the-art deep learning classifiers trained to yield diagnostic labels from X-ray images are biased with re…

2020-02-14abs ↗pdf ↗

Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlationsstat.ML

TTLSA adapts models to label shifts across domains with nuisance factors.

problem Adapting models to changes in label distributions with nuisance factors.

method TTLSA uses EM on unlabeled samples to adapt a trained model to new label distributions.

result TTLSA improves model performance over invariance methods and baseline methods.

label shift test-time adaptation nuisance factors EM algorithm image datasets

2022-11-28abs ↗pdf ↗

Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classificationcs.LG

New DAM method improves AUC scores in medical image classification.

problem Maximizing AUC in large-scale medical image classification.

method Proposes AUC margin loss for robust optimization, conducts extensive empirical studies.

result Improves performance on four medical image classification tasks, achieving 1st place on Stanford CheXpert.

Deep AUC Maximization Margin Loss Medical Image Classification AUC Maximization Robust Optimization

2020-12-06abs ↗pdf ↗