activity
20172022
most citedmustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesis

10 citations · 28 across the 7 of their papers we have counts for

collaborators

12 papers

eess.IV20222 cited

Content-Based Medical Image Retrieval with Opponent Class Adaptive Margin Loss

Şaban Öztürk, Emin Celik, Tolga Cukur

Broadspread use of medical imaging devices with digital storage has paved the way for curation of substantial data repositories. Fast access to image samples with similar appearanc…

eess.IV20223 cited

Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets

Saban Ozturk, Tolga Cukur

Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for th…

eess.IV20218 cited

Constrained Ellipse Fitting for Efficient Parameter Mapping with Phase-cycled bSSFP MRI

Kübra Keskin, Uğur Yılmaz, Tolga Çukur

Balanced steady-state free precession (bSSFP) imaging enables high scan efficiency in MRI, but differs from conventional sequences in terms of elevated sensitivity to main field in…

cs.CV20212 cited

A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors

Salman Ul Hassan Dar, Mahmut Yurt, Tolga Çukur

Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction. DNNs typically learn data-driven priors from large datasets constituting pairs of un…

eess.IV20203 cited

Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks

Muzaffer Özbey, Mahmut Yurt, Salman Ul Hassan Dar +1

Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input. Cross-sectional models can decrease the model complexity…

eess.IV201910 cited

mustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesis

Mahmut Yurt, Salman Ul Hassan Dar, Aykut Erdem +2

Multi-contrast MRI protocols increase the level of morphological information available for diagnosis. Yet, the number and quality of contrasts is limited in practice by various fac…