Browsing by Author "Ul Hassan Dar, Salman"

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Open Access
BolT: Fused window transformers for fMRI time series analysis
(Elsevier B.V., 2023-05-18) Bedel, Hasan Atakan; Şıvgın, Irmak; Dalmaz, Onat; Ul Hassan Dar, Salman ; Çukur, Tolga
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 representations across diverse time scales. Here, we present BolT, a blood-oxygen-level-dependent transformer model, for analyzing multi-variate fMRI time series. BolT leverages a cascade of transformer encoders equipped with a novel fused window attention mechanism. Encoding is performed on temporally-overlapped windows within the time series to capture local representations. To integrate information temporally, cross-window attention is computed between base tokens in each window and fringe tokens from neighboring windows. To gradually transition from local to global representations, the extent of window overlap and thereby number of fringe tokens are progressively increased across the cascade. Finally, a novel cross-window regularization is employed to align high-level classification features across the time series. Comprehensive experiments on large-scale public datasets demonstrate the superior performance of BolT against state-of-the-art methods. Furthermore, explanatory analyses to identify landmark time points and regions that contribute most significantly to model decisions corroborate prominent neuroscientific findings in the literature.
Open Access
Improving image synthesis quality in multi-contrast MRI using transfer learning via autoencoders
(IEEE, 2022-08-29) Selçuk, Şahan Yoruç; Dalmaz, Onat; Ul Hassan Dar, Salman; Çukur, Tolga
The capacity of magnetic resonance imaging (MRI) to capture several contrasts within a session enables it to obtain increased diagnostic information. However, such multi-contrast MRI tests take a long time to scan, resulting in acquiring just a part of the essential contrasts. Synthetic multi-contrast MRI has the potential to improve radiological observations and consequent image analysis activities. Because of its ability to generate realistic results, generative adversarial networks (GAN) have recently been the most popular choice for medical imaging synthesis. This paper proposes a novel generative adversarial framework to improve the image synthesis quality in multi-contrast MRI. Our method uses transfer learning to adapt pre-trained autoencoder networks to the synthesis task and enhances the image synthesis quality by initializing the training process with more optimal network parameters. We demonstrate that the proposed method outperforms competing synthesis models by 0.95 dB on average on a well-known multi-contrast MRI dataset.