22 citations · 66 across the 22 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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…