602 citations · 816 across the 24 of their papers we have counts for
28 papers
Self-Consistent Recursive Diffusion Bridge for Medical Image Translation
Fuat Arslan, Bilal Kabas, Onat Dalmaz +2
Denoising diffusion models (DDM) have gained recent traction in medical image translation given improved training stability over adversarial models. DDMs learn a multi-step denoisi…
I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling
Omer F. Atli, Bilal Kabas, Fuat Arslan +4
Multi-modal medical image synthesis involves nonlinear transformation of tissue signals between source and target modalities, where tissues exhibit contextual interactions across d…
DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRI
Hasan Atakan Bedel, Tolga Çukur
Deep learning analyses have offered sensitivity leaps in detection of cognitive states from functional MRI (fMRI) measurements across the brain. Yet, as deep models perform hierarc…
Self-Supervised MRI Reconstruction with Unrolled Diffusion Models
Yilmaz Korkmaz, Tolga Cukur, Vishal M. Patel
Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality. Promising deep learning methods have recently been propo…
FD-Net: An Unsupervised Deep Forward-Distortion Model for Susceptibility Artifact Correction in EPI
Abdallah Zaid Alkilani, Tolga Çukur, Emine Ulku Saritas
Recent learning-based correction approaches in EPI estimate a displacement field, unwarp the reversed-PE image pair with the estimated field, and average the unwarped pair to yield…
JointNET: A Deep Model for Predicting Active Sacroiliitis from Sacroiliac Joint Radiography
Sevcan Turk, Ahmet Demirkaya, M Yigit Turali +9
Purpose: To develop a deep learning model that predicts active inflammation from sacroiliac joint radiographs and to compare the success with radiologists. Materials and Methods: A…
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…
Unsupervised Simplification of Legal Texts
Mert Cemri, Tolga Çukur, Aykut Koç
The processing of legal texts has been developing as an emerging field in natural language processing (NLP). Legal texts contain unique jargon and complex linguistic attributes in…
COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers
Idil Aytekin, Onat Dalmaz, Kaan Gonc +5
Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening…
BolT: Fused Window Transformers for fMRI Time Series Analysis
Hasan Atakan Bedel, Irmak Şıvgın, Onat Dalmaz +2
Deep-learning models have enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for contextual…
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…
ResViT: Residual vision transformers for multi-modal medical image synthesis
Onat Dalmaz, Mahmut Yurt, Tolga Çukur
Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. Howeve…
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…
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Yilmaz Korkmaz, Salman UH Dar, Mahmut Yurt +2
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding th…
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…
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…
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery
Mahmut Yurt, Muzaffer Özbey, Salman Ul Hassan Dar +3
Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits o…
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…
Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions
Emre Kopanoglu, Alper Güngör, Toygan Kilic +4
Multi-contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time-efficient strategy to acqu…
Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI
Thomas Sanchez, Baran Gözcü, Ruud B. van Heeswijk +4
Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data. In this pape…
Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI
Salman Ul Hassan Dar, Mahmut Yurt, Mohammad Shahdloo +2
Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available for diagnosis. Yet, excessive scan times associated with additional contrasts may be a l…
Learning-Based Compressive MRI
Baran Gözcü, Rabeeh Karimi Mahabadi, Yen-Huan Li +4
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling pat…
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
Salman Ul Hassan Dar, Mahmut Yurt, Levent Karacan +3
Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, scan time limitations may pro…
Semantic Structure and Interpretability of Word Embeddings
Lutfi Kerem Senel, Ihsan Utlu, Veysel Yucesoy +2
Dense word embeddings, which encode semantic meanings of words to low dimensional vector spaces have become very popular in natural language processing (NLP) research due to their…