SUMI distills photon-counting CT quality into routine chest CT by learning to reverse clinically validated acquisition degradations, yielding 15-20% gains in image metrics, better radiologist utility, and up to 15% higher lesion detection sensitivity.
In: Proceedings of the IEEE conference on computer vision and pattern recognition
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
cGAN with atrous convolutions and channel weighting segments breast tumors in ultrasound at 93.76% Dice and 88.82% IoU, then classifies benign vs malignant at 85% accuracy using boundary shape features.
citing papers explorer
-
Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling
SUMI distills photon-counting CT quality into routine chest CT by learning to reverse clinically validated acquisition degradations, yielding 15-20% gains in image metrics, better radiologist utility, and up to 15% higher lesion detection sensitivity.
-
An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning
cGAN with atrous convolutions and channel weighting segments breast tumors in ultrasound at 93.76% Dice and 88.82% IoU, then classifies benign vs malignant at 85% accuracy using boundary shape features.