32 citations · 50 across the 6 of their papers we have counts for
6 papers
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert +18
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains un…
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
Muhammad U. Mirza, Onat Dalmaz, Hasan A. Bedel +5
Deep generative models have gained recent traction in accelerated MRI reconstruction. Diffusion priors are particularly promising given their representational fidelity. Instead of…
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…
A plug-in graph neural network to boost temporal sensitivity in fMRI analysis
Irmak Sivgin, Hasan A. Bedel, Şaban Öztürk +1
Learning-based methods have recently enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) time series. Deep learning models that receive as input functio…
Unsupervised Medical Image Translation with Adversarial Diffusion Models
Muzaffer Özbey, Onat Dalmaz, Salman UH Dar +4
Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images invol…
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…