22 citations · 66 across the 22 of their papers we have counts for
27 papers
Sharpening the Ensemble: An SSIM-Aligned Residual Refiner for Brain-MRI Inpainting Post-Processing
Kubilay Kağan Kömürcü, İlkay Öksüz
Brain-MRI inpainting replaces a masked region of a scan with synthesized, anatomically plausible healthy tissue, so that analysis tools built for healthy brains can be applied to i…
Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI
Ruru Xu, Kian Anvari Hamedani, Zhikai Yang +1
Active sampling for accelerated MRI must distribute a tight sampling budget across spatial frequencies that carry very different kinds of information. Low frequencies hold most of…
HierAdaptMR: Cross-Center Cardiac MRI Reconstruction with Hierarchical Feature Adapters
Ruru Xu, Ilkay Oksuz
Deep learning-based cardiac MRI reconstruction faces significant domain shift challenges when deployed across multiple clinical centers with heterogeneous scanner configurations an…
Adaptive k-space Radial Sampling for Cardiac MRI with Reinforcement Learning
Ruru Xu, Ilkay Oksuz
Accelerated Magnetic Resonance Imaging (MRI) requires careful optimization of k-space sampling patterns to balance acquisition speed and image quality. While recent advances in dee…
Beyond Pixels: Medical Image Quality Assessment with Implicit Neural Representations
Caner Özer, Patryk Rygiel, Bram de Wilde +2
Artifacts pose a significant challenge in medical imaging, impacting diagnostic accuracy and downstream analysis. While image-based approaches for detecting artifacts can be effect…
Pic2Diagnosis: A Method for Diagnosis of Cardiovascular Diseases from the Printed ECG Pictures
Oğuzhan Büyüksolak, İlkay Öksüz
The electrocardiogram (ECG) is a vital tool for diagnosing heart diseases. However, many disease patterns are derived from outdated datasets and traditional stepwise algorithms wit…
HyperCMR: Enhanced Multi-Contrast CMR Reconstruction with Eagle Loss
Ruru Xu, Caner Özer, Ilkay Oksuz
Accelerating image acquisition for cardiac magnetic resonance imaging (CMRI) is a critical task. CMRxRecon2024 challenge aims to set the state of the art for multi-contrast CMR rec…
Mammographic Breast Positioning Assessment via Deep Learning
Toygar Tanyel, Nurper Denizoglu, Mustafa Ege Seker +5
Breast cancer remains a leading cause of cancer-related deaths among women worldwide, with mammography screening as the most effective method for the early detection. Ensuring prop…
GLIMS: Attention-Guided Lightweight Multi-Scale Hybrid Network for Volumetric Semantic Segmentation
Ziya Ata Yazıcı, İlkay Öksüz, Hazım Kemal Ekenel
Convolutional Neural Networks (CNNs) have become widely adopted for medical image segmentation tasks, demonstrating promising performance. However, the inherent inductive biases in…
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
Ziya Ata Yazıcı, İlkay Öksüz, Hazım Kemal Ekenel
Glioblastoma is a highly aggressive and malignant brain tumor type that requires early diagnosis and prompt intervention. Due to its heterogeneity in appearance, developing automat…
Interpretable ECG Analysis for Myocardial Infarction Detection through Counterfactuals
Toygar Tanyel, Sezgin Atmaca, Kaan Gökçe +4
In the evolving landscape of ECG signal analysis, the challenge of limited transparency in machine learning models remains a significant barrier to their effective integration into…
Explainable Image Quality Assessment for Medical Imaging
Caner Ozer, Arda Guler, Aysel Turkvatan Cansever +1
Medical image quality assessment is an important aspect of image acquisition, as poor-quality images may lead to misdiagnosis. Manual labelling of image quality is a tedious task f…
Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI using Histogram Matching
Mahyar Bolhassani, Ilkay Oksuz
Automatic segmentation of the heart cavity is an essential task for the diagnosis of cardiac diseases. In this paper, we propose a semi-supervised segmentation setup for leveraging…
Prostate Lesion Estimation using Prostate Masks from Biparametric MRI
Ahmet Karagoz, Mustafa Ege Seker, Mert Yergin +5
Biparametric MRI has emerged as an alternative to multiparametric prostate MRI, which eliminates the need for the potential harms to the patient due to the contrast medium. One maj…
Shifted Windows Transformers for Medical Image Quality Assessment
Caner Ozer, Arda Guler, Aysel Turkvatan Cansever +3
To maintain a standard in a medical imaging study, images should have necessary image quality for potential diagnostic use. Although CNN-based approaches are used to assess the ima…
A survey on shape-constraint deep learning for medical image segmentation
Simon Bohlender, Ilkay Oksuz, Anirban Mukhopadhyay
Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep learning based medical image segmenta…
Channel Attention Networks for Robust MR Fingerprinting Matching
Refik Soyak, Ebru Navruz, Eda Ozgu Ersoy +5
Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varyin…
Transfer Learning for Electricity Price Forecasting
Salih Gunduz, Umut Ugurlu, Ilkay Oksuz
Electricity price forecasting is an essential task in all the deregulated markets of the world. The accurate prediction of the day-ahead electricity prices is an active research fi…
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
Jo Schlemper, Ilkay Oksuz, James R. Clough +5
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the…
Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders
Esther Puyol-Antón, Bram Ruijsink, James R. Clough +4
Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of differ…
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
lkay Oksuz, James Clough, Bram Ruijsink +7
In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose…
Global and Local Interpretability for Cardiac MRI Classification
James R. Clough, Ilkay Oksuz, Esther Puyol-Anton +3
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their app…
Mechanically Powered Motion Imaging Phantoms: Proof of Concept
Alberto Gomez, Cornelia Schmitz, Markus Henningsson +8
Motion imaging phantoms are expensive, bulky and difficult to transport and set-up. The purpose of this paper is to demonstrate a simple approach to the design of multi-modality mo…
Explicit topological priors for deep-learning based image segmentation using persistent homology
James R. Clough, Ilkay Oksuz, Nicholas Byrne +2
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our meth…