# Structural Graph Neural Networks with Anatomical Priors for Explainable Chest X-ray Diagnosis

arXiv: https://arxiv.org/abs/2601.11987 · HTML: https://mlsanity.com/paper/2601.11987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [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)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification](https://mlsanity.com/paper/2305.18221/) (2023)

## Abstract (arXiv)

We present a structural graph reasoning framework that incorporates explicit anatomical priors for explainable vision-based diagnosis. Convolutional feature maps are reinterpreted as patch-level graphs, where nodes encode both appearance and spatial coordinates, and edges reflect local structural adjacency. Unlike conventional graph neural networks that rely on generic message passing, we introduce a custom structural propagation mechanism that explicitly models relative spatial relations as part of the reasoning process. This design enables the graph to act as an inductive bias for structured inference rather than a passive relational representation. The proposed model jointly supports node-level lesion-aware predictions and graph-level diagnostic reasoning, yielding intrinsic explainability through learned node importance scores without relying on post-hoc visualization techniques. We demonstrate the approach through a chest X-ray case study, illustrating how structural priors guide relational reasoning and improve interpretability. While evaluated in a medical imaging context, the framework is domain-agnostic and aligns with the broader vision of graph-based reasoning across artificial intelligence systems. This work contributes to the growing body of research exploring graphs as computational substrates for structure-aware and explainable learning.