VHSMarker and the Canine Cardiac Keypoint (CCK) Dataset: A Benchmark for Veterinary Cardiac X-ray Analysis

ICLR 2026 Conference Submission15774 Authors

Keywords: Veterinary AI, Canine Cardiology, Keypoint Detection, Benchmark Datasets, State-Space Models

TL;DR: We release a large canine X-ray dataset and an easy-to-use annotation tool, together with a strong benchmark model, to advance automated heart disease analysis in veterinary medicine.

Abstract: We present VHSMarker, a web-based annotation tool that enables rapid and standardized labeling of six cardiac key points in canine thoracic radiographs. VHSMarker reduces annotation time to 10–12 seconds per image while supporting real-time vertebral heart score (VHS) calculation, model-assisted prediction, and quality control. Using this tool, we constructed the Canine Cardiac Key Point (CCK) Dataset, a large-scale benchmark of 21,465 annotated radiographs from 12,385 dogs across 144 breeds and additional mixed breed cases, making it the largest curated resource for canine cardiac analysis to date. To demonstrate the utility of this dataset, we introduce MambaVHS, a baseline model that integrates Mamba blocks for long-range sequence modeling with convolutional layers for local spatial precision. MambaVHS achieves 91.8\% test accuracy, surpassing 13 strong baselines including ConvNeXt and EfficientNetB7, and establishes state-space modeling as a promising direction for veterinary imaging. Together, the tool, dataset, and baseline model provide the first reproducible benchmark for automated VHS estimation and a foundation for future research in veterinary cardiology. The source code and dataset are available on our project website: https://anonymousgenai.github.io/vhsmarker.

Primary Area: datasets and benchmarks

Submission Number: 15774