Encoding of Demographic and Anatomical Information in Chest X-Ray-based Severe Left Ventricular Hypertrophy Classifiers
- URL: http://arxiv.org/abs/2506.03192v1
- Date: Sat, 31 May 2025 13:30:04 GMT
- Title: Encoding of Demographic and Anatomical Information in Chest X-Ray-based Severe Left Ventricular Hypertrophy Classifiers
- Abstract summary: We introduce a direct classification framework that predicts severe left ventricular hypertrophy from chest X-rays.<n>Our approach achieves high AUROC and AUPRC, and employs Mutual Information Neural Estimation to quantify feature expressivity.
- Score: 36.052936348670634
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation [66.62662330558906]
CORAL is a multimodal framework that integrates spatial grounding and concept-level supervision into a unified reasoning process.<n>CORAL employs a prompt-driven medical segmentation model to localize lesions and predicts multi-class clinical attributes through a Concept Bottleneck module.<n>Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs.
arXiv Detail & Related papers (2026-09-14T10:21:23Z) - Beyond Fluency: A Clinical Benchmark and Anomaly-Enhanced Baseline for Spine MRI Report Generation [11.89113336167412]
We benchmark state-of-the-art Vision-Language Models (VLMs) on lumbar spine MRI with a focus on diagnostic accuracy.<n>We propose an architecture-agnostic framework that augments VLM inputs with spatially localized, disc-level anomaly heatmaps.
arXiv Detail & Related papers (2026-08-07T11:21:33Z) - Which Anatomy Matters Under Limited Labels? A Data-Efficient Anatomy-Aware Benchmark for Cardiac Pathology Prediction [1.203534353113171]
We use a low-data anatomy-aware benchmark for 5-class cardiac pathology prediction on the public ACDC MRI dataset.<n>We find that under limited label settings, representation dominates complexity.<n>These results suggest that in resource-constrained healthcare settings, identifying and representing the most informative anatomy may matter more than the increasing complexity of the model alone.
arXiv Detail & Related papers (2026-05-25T11:03:53Z) - CORA: A Pathology Synthesis Driven Foundation Model for Coronary CT Angiography Analysis and MACE Risk Assessment [7.00689933116151]
CORA is a 3D vision foundation model for comprehensive cardiovascular risk assessment.<n>We trained CORA on a large-scale cohort of 12,801 unlabeled coronary computed tomography angiography volumes.<n> CORA consistently outperformed state-of-the-art 3D vision foundation models, achieving up to a 29% performance gain.
arXiv Detail & Related papers (2026-03-25T22:28:31Z) - Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data [76.89269238957593]
Coronary artery stenosis is a leading cause of cardiovascular disease, diagnosed by analyzing the coronary arteries from multiple angiography views.<n>We propose SegmentMIL, a transformer-based multi-view multiple-instance learning framework for patient-level stenosis classification.
arXiv Detail & Related papers (2026-02-02T13:07:52Z) - CXR-LanIC: Language-Grounded Interpretable Classifier for Chest X-Ray Diagnosis [4.309108879640932]
Deep learning models have achieved remarkable accuracy in chest X-ray diagnosis, yet their widespread clinical adoption remains limited by the black-box nature of their predictions.<n>We introduce CXRLanIC, a novel framework that addresses this interpretability challenge through task-aligned medical pattern discovery.
arXiv Detail & Related papers (2025-10-24T13:46:18Z) - CardiacCLIP: Video-based CLIP Adaptation for LVEF Prediction in a Few-shot Manner [14.429336783145644]
Left ventricular ejection fraction (LVEF) serves as a key indicator of heart function.<n>Existing LVEF estimation methods depend on large-scale annotated video datasets.<n>We propose CardiacCLIP, a video-based framework that enhances LVEF prediction through attention-based frame aggregation and multi-resolution input scaling.
arXiv Detail & Related papers (2025-09-21T12:52:08Z) - Classification, Regression and Segmentation directly from k-Space in Cardiac MRI [11.690226907936903]
We propose KMAE, a Transformer-based model specifically designed to process k-space data directly.
KMAE can handle critical cardiac disease classification, relevant phenotype regression, and cardiac segmentation tasks.
We utilize this model to investigate the potential of k-space-based diagnosis in cardiac MRI.
arXiv Detail & Related papers (2024-07-29T15:35:35Z) - Continuous max-flow augmentation of self-supervised few-shot learning on SPECT left ventricles [0.0]
This paper aims to give a recipe for diagnostic centers as well as for clinics to automatically segment the myocardium based on small and low-quality labels on reconstructed SPECT.
A combination of Continuous Max-Flow (CMF) with prior shape information is developed to augment the 3D U-Net self-supervised learning (SSL) approach on various geometries of SPECT apparatus.
arXiv Detail & Related papers (2024-05-09T03:19:19Z) - Attribute Regularized Soft Introspective Variational Autoencoder for
Interpretable Cardiac Disease Classification [2.4828003234992666]
Interpretability is essential to ensure that clinicians can comprehend and trust artificial intelligence models.
We propose a novel interpretable approach that combines attribute regularization of the latent space within the framework of an adversarially trained variational autoencoder.
arXiv Detail & Related papers (2023-12-14T13:20:57Z) - SSASS: Semi-Supervised Approach for Stenosis Segmentation [9.767759441883008]
The complexity of coronary artery structures combined with the inherent noise in X-ray images poses a considerable challenge to this task.
We introduce a semi-supervised approach for cardiovascular stenosis segmentation.
Our approach demonstrated an exceptional performance in the Automatic Region-based Coronary Artery Disease diagnostics.
arXiv Detail & Related papers (2023-11-17T02:01:19Z) - K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality
Assessment [71.27193056354741]
The problem of how to assess cross-modality medical image synthesis has been largely unexplored.
We propose a new metric K-CROSS to spur progress on this challenging problem.
K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
arXiv Detail & Related papers (2023-07-10T01:26:48Z) - Preservation of High Frequency Content for Deep Learning-Based Medical
Image Classification [74.84221280249876]
An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists.
We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
arXiv Detail & Related papers (2022-05-08T15:29:54Z) - Improving Classification Model Performance on Chest X-Rays through Lung
Segmentation [63.45024974079371]
We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations.
Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
arXiv Detail & Related papers (2022-02-22T15:24:06Z) - Segmentation of Anatomical Layers and Artifacts in Intravascular
Polarization Sensitive Optical Coherence Tomography Using Attending Physician
and Boundary Cardinality Lost Terms [4.93836246080317]
Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses.
We propose a convolutional neural network model and optimize its performance using a new multi-term loss function.
Our model segments two classes of major artifacts and detects the anatomical layers within the thickened vessel wall regions.
arXiv Detail & Related papers (2021-05-11T15:52:31Z) - Variational Knowledge Distillation for Disease Classification in Chest
X-Rays [102.04931207504173]
We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays.
We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
arXiv Detail & Related papers (2021-03-19T14:13:56Z) - A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced
Cardiac Magnetic Resonance Imaging [90.29017019187282]
" 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset.
Analyse of the submitted algorithms using technical and biological metrics was performed.
Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
arXiv Detail & Related papers (2020-04-26T08:49:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.