mlsanityData to 14 Oct 2026

CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays

arXiv 2208.03873 ↗First posted 8 Aug 2022cs.CV cs.LGcited by 19

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Abstract (arXiv)

Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention. Monitoring the progression of pathologies that are visualized through chest imaging poses several challenges in anatomical motion estimation and image registration, i.e., spatially aligning the two images and modeling temporal dynamics in change detection. In this work, we propose CheXRelNet, a neural model that can track longitudinal pathology change relations between two CXRs. CheXRelNet incorporates local and global visual features, utilizes inter-image and intra-image anatomical information, and learns dependencies between anatomical region attributes, to accurately predict disease change for a pair of CXRs. Experimental results on the Chest ImaGenome dataset show increased downstream performance compared to baselines. Code is available at https://github.com/PLAN-Lab/ChexRelNet

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 8 Aug 2022.

Builds on

12 of 32 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Graph Attention Networkscited by 8,923
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  5. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  6. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
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  8. 2016An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wildcited by 146
  9. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  10. 2021Chest ImaGenome Dataset for Clinical Reasoningcited by 90
  11. 2020Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Developmentcited by 56
  12. 2021AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-raycited by 19

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 6; 2026: 8.

19 papers.

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  1. 2023Learning to Exploit Temporal Structure for Biomedical Vision-Language Processingcited by 211
  2. 2025Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generationcited by 42
  3. 2024HERGen: Elevating Radiology Report Generation with Longitudinal Datacited by 33
  4. 2023Controllable Chest X-Ray Report Generation from Longitudinal Representationscited by 23
  5. 2024Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generationcited by 11
  6. 2025CoCa-CXR: Contrastive Captioners Learn Strong Temporal Structures for Chest X-Ray Vision-Language Understandingcited by 11
  7. 2025Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Modelscited by 5
  8. 2026XrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosiscited by 5
  9. 2025Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuningcited by 4
  10. 2023Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methodscited by 2
  11. 2026TRACE: Temporal Radiology with Anatomical Change Explanation for Grounded X-ray Report Generationcited by 2
  12. 2026Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuningcited by 2
7 more
  1. 2025CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detectioncited by 1
  2. 2025Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignmentcited by 1
  3. 2026Temporal Inversion for Learning Interval Change in Chest X-Rayscited by 1
  4. 2026Beyond Masks: The Case for Medical Image Parsingcited by 0
  5. 2026GraphVid: Interactive Graph-Controllable Video Generationcited by 0
  6. 2026CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographscited by 0
  7. 2026CheXGround: Anatomical Region Tokens for Grounded Longitudinal Chest X-ray Interpretationcited by 0