Joint signal-domain segmentation and reconstruction for photoacoustic tomography via physics-informed self-supervised learning
JIRSSD-Net eliminates the error propagation of conventional sequential pipelines and enhances robustness and accuracy for preclinical PAT applications and outperforming image-domain, signal-domain, and joint baselines across all metrics with statistical significance confirmed by the Friedman test.