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20222026
most citedMedical Image Segmentation Review: The success of U-Net

97 citations · 125 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

TRIUNE-Net: Harmonizing Scale, Shape, and Efficiency in Pancreatic Tumor Segmentation

Amir Hossein Saleknia, Alireza Kheyrkhah, Sanaz Karimijafarbigloo +5

Pancreatic tumor segmentation in 3D CT volumes is challenged by extreme scale variability across both the pancreas and tumor, and highly irregular tumor morphology. While recent ad…

cs.CV2026

Harmonized Feature Conditioning and Frequency-Prompt Personalization for Multi-Rater Medical Segmentation

Sanaz Karimijafarbigloo, Armin Khosravi, Alireza Kheyrkhah +3

Multi-rater medical image segmentation captures the inherent ambiguity of clinical interpretation, where diagnostic boundaries vary across experts and imaging devices. Existing app…

cs.CV2023★ 23 cited

Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook

Reza Azad, Moein Heidary, Kadir Yilmaz +5

Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predomi…

cs.CV2023

FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation

Amirhossein Kazerouni, Sanaz Karimijafarbigloo, Reza Azad +3

Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training. In response to this challenge, we introduce Fu…

cs.CV2023

Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning

Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko +2

Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-la…

cs.CV2023

Self-supervised Semantic Segmentation: Consistency over Transformation

Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni +3

Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical…