3 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…
cs.CV2024
Modified CycleGAN for the synthesization of samples for wheat head segmentation
Jaden Myers, Keyhan Najafian, Farhad Maleki +1
Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily…
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…