8 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls
Farhad Maleki, Katie Ovens, Rajiv Gupta +3
Purpose: Despite the potential of machine learning models, the lack of generalizability has hindered their widespread adoption in clinical practice. We investigate three methodolog…