HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection. (arXiv:2210.02241v1 [eess.IV])

Advances in data-driven deep learning for chest X-ray image analysis
underscore the need for explainability, privacy, large datasets and significant
computational resources. We frame privacy and explainability as a lossy
single-image compression problem to reduce both computational and data
requirements without training. For Cardiomegaly detection in chest X-ray
images, we propose HeartSpot and four spatial bias priors. HeartSpot priors
define how to sample pixels based on domain knowledge from medical literature
and from machines. HeartSpot privatizes chest X-ray images by …

compression data detection