Onat Dalmaz

Bilkent University · Department of Electrical & Electronic Engineering

MRI translation models learn a mapping from an acquired source contrast to an unavailable target contrast. Collaboration between institutes is essential to train translation models that can generalize across diverse datasets. That said, aggregating all imaging data and training a centralized model poses privacy problems. Recently, federated learnin...

Learning-based translation between MRI contrasts involves supervised deep models trained using high-quality source- and target-contrast images derived from fully-sampled acquisitions, which might be difficult to collect under limitations on scan costs or time. To facilitate curation of training sets, here we introduce the first semi-supervised mode...

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involve respiratory assessments. While auscultation is a mainstream method for symptomatic monitoring, its diagnostic utility is hampered by the need for dedicated hospital visits. Continual remote monitoring based on recordings of respiratory sounds on portable devices i...

Imputation of missing images via source-to-target modality translation can facilitate downstream tasks in medical imaging. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fid...

Learning-based MRI translation involves a synthesis model that maps a source-contrast onto a target-contrast image. Multi-institutional collaborations are key to training synthesis models across broad datasets, yet centralized training involves privacy risks. Federated learning (FL) is a collaboration framework that instead adopts decentralized tra...

Functional magnetic resonance imaging (fMRI) enables examination of inter-regional interactions in the brain via functional connectivity (FC) analyses that measure the synchrony between the temporal activations of separate regions. Given their exceptional sensitivity, deep-learning methods have received growing interest for FC analyses of high-dime...

Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel gener...

Multi-modal imaging is a key healthcare technology in the diagnosis and management of disease, but it is often underutilized due to costs associated with multiple separate scans. This limitation yields the need for synthesis of unacquired modalities from the subset of available modalities. In recent years, generative adversarial network (GAN) model...