2 citations · 2 across the 3 of their papers we have counts for
3 papers
Performance Estimation for Supervised Medical Image Segmentation Models on Unlabeled Data Using UniverSeg
Jingchen Zou, Jianqiang Li, Gabriel Jimenez +5
The performance of medical image segmentation models is usually evaluated using metrics like the Dice score and Hausdorff distance, which compare predicted masks to ground truth an…
ConfIC-RCA: Statistically Grounded Efficient Estimation of Segmentation Quality
Matias Cosarinsky, Ramiro Billot, Lucas Mansilla +5
Assessing the quality of automatic image segmentation is crucial in clinical practice, but often very challenging due to the limited availability of ground truth annotations. Rever…
Comparative Analysis of ImageNet Pre-Trained Deep Learning Models and DINOv2 in Medical Imaging Classification
Yuning Huang, Jingchen Zou, Lanxi Meng +7
Medical image analysis frequently encounters data scarcity challenges. Transfer learning has been effective in addressing this issue while conserving computational resources. The r…