Encoding of Demographic and Anatomical Information in Chest X-Ray-based Severe Left Ventricular Hypertrophy Classifiers
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Abstract (arXiv)
While echocardiography and MRI are clinical standards for evaluating cardiac structure, their use is limited by cost and accessibility.We introduce a direct classification framework that predicts severe left ventricular hypertrophy from chest X-rays, without relying on anatomical measurements or demographic inputs. Our approach achieves high AUROC and AUPRC, and employs Mutual Information Neural Estimation to quantify feature expressivity. This reveals clinically meaningful attribute encoding and supports transparent model interpretation.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 31 May 2025.
Builds on
8 of 19 references.
References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
- 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
- 2018MINE: Mutual Information Neural Estimationcited by 683
- 2022Delving into Masked Autoencoders for Multi-Label Thorax Disease Classificationcited by 65
- 2019How are attributes expressed in face DCNNs?cited by 13
- 2025A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identificationcited by 2
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Citing papers per year: 2026: 2.
2 papers.