2 papers
eess.IV2025
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net
Seyed Sina Ziaee, Farhad Maleki, Katie Ovens
Accurate and reliable tumor segmentation is essential in medical imaging analysis for improving diagnosis, treatment planning, and monitoring. However, existing segmentation models…
eess.IV2024
RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models
Farhad Maleki, Linda Moy, Reza Forghani +14
Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of…