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

arXiv: https://arxiv.org/abs/2208.03873 · HTML: https://mlsanity.com/paper/2208.03873/

## 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 (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild](https://mlsanity.com/paper/1605.04253/) (2016)
- [TorchXRayVision: A library of chest X-ray datasets and models](https://mlsanity.com/paper/2111.00595/) (2021)
- [Chest ImaGenome Dataset for Clinical Reasoning](https://mlsanity.com/paper/2108.00316/) (2021)
- [Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development](https://mlsanity.com/paper/2009.07386/) (2020)
- [AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray](https://mlsanity.com/paper/2105.09937/) (2021)

## Built on by (most cited first, 19 of 19)

- [Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing](https://mlsanity.com/paper/2301.04558/) (2023)
- [Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation](https://mlsanity.com/paper/2502.20056/) (2025)
- [HERGen: Elevating Radiology Report Generation with Longitudinal Data](https://mlsanity.com/paper/2407.15158/) (2024)
- [Controllable Chest X-Ray Report Generation from Longitudinal Representations](https://mlsanity.com/paper/2310.05881/) (2023)
- [Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation](https://mlsanity.com/paper/2405.09586/) (2024)
- [CoCa-CXR: Contrastive Captioners Learn Strong Temporal Structures for Chest X-Ray Vision-Language Understanding](https://mlsanity.com/paper/2502.20509/) (2025)
- [Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models](https://mlsanity.com/paper/2506.08990/) (2025)
- [XrayClaw: Cooperative-Competitive Multi-Agent Alignment for Trustworthy Chest X-ray Diagnosis](https://mlsanity.com/paper/2604.02695/) (2026)
- [Walking the Tightrope: Disentangling Beneficial and Detrimental Drifts in Non-Stationary Custom-Tuning](https://mlsanity.com/paper/2505.13081/) (2025)
- [Employing similarity to highlight differences: On the impact of anatomical assumptions in chest X-ray registration methods](https://mlsanity.com/paper/2301.09338/) (2023)
- [TRACE: Temporal Radiology with Anatomical Change Explanation for Grounded X-ray Report Generation](https://mlsanity.com/paper/2602.02963/) (2026)
- [Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning](https://mlsanity.com/paper/2604.15705/) (2026)
- [CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection](https://mlsanity.com/paper/2505.06903/) (2025)
- [Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment](https://mlsanity.com/paper/2510.11112/) (2025)
- [Temporal Inversion for Learning Interval Change in Chest X-Rays](https://mlsanity.com/paper/2604.04563/) (2026)
- [Beyond Masks: The Case for Medical Image Parsing](https://mlsanity.com/paper/2605.11438/) (2026)
- [GraphVid: Interactive Graph-Controllable Video Generation](https://mlsanity.com/paper/2607.21580/) (2026)
- [CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs](https://mlsanity.com/paper/2608.28137/) (2026)
- [CheXGround: Anatomical Region Tokens for Grounded Longitudinal Chest X-ray Interpretation](https://mlsanity.com/paper/2608.30758/) (2026)

## 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