Explaining Chest X-ray Pathology Models using Textual Concepts
<|reference_start|>Explaining Chest X-ray Pathology Models using Textual Concepts: Deep learning models have revolutionized medical imaging and diagnostics, yet their opaque nature poses challenges for clinical adoption and trust. Amongst approaches to improve model interpretability, concept-based explanations aim to provide concise and human understandable explanations of any arbitrary classifier. However, such methods usually require a large amount of manually collected data with concept annotation, which is often scarce in the medical domain. In this paper, we propose Conceptual Counterfactual Explanations for Chest X-ray (CoCoX) that leverage existing vision-language models (VLM) joint embedding space to explain black-box classifier outcomes without the need for annotated datasets. Specifically, we utilize textual concepts derived from chest radiography reports and a pre-trained chest radiography-based VLM to explain three common cardiothoracic pathologies. We demonstrate that the explanations generated by our method are semantically meaningful and faithful to underlying pathologies.<|reference_end|>
@article{sadashivaiah2024explaining,
    title={Explaining Chest X-ray Pathology Models using Textual Concepts},
    author={Vijay Sadashivaiah, Pingkun Yan, and James A. Hendler},
    journal={arXiv preprint arXiv:2407.00557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00557},
    primaryClass={cs.CV}
}