LondonU-Net model hit 61% F1 on stenosis segmentation.
Standard losses fail when blockage pixels are rare; a custom loss corrected the bias, lifting benchmark F1 from 53% to 61%, researchers found.
The researchers say automating stenosis detection would reduce reliance on specialist cardiologists, who currently read each angiogram manually.
Sources: Nature