83 research outputs found
BolT: Fused Window Transformers for fMRI Time Series Analysis
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
Unsupervised Medical Image Translation with Adversarial Diffusion Models
Imputation of missing images via source-to-target modality translation can
improve diversity in medical imaging protocols. 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 fidelity. Here, we propose a
novel method based on adversarial diffusion modeling, SynDiff, for improved
performance in medical image translation. To capture a direct correlate of the
image distribution, SynDiff leverages a conditional diffusion process that
progressively maps noise and source images onto the target image. For fast and
accurate image sampling during inference, large diffusion steps are taken with
adversarial projections in the reverse diffusion direction. To enable training
on unpaired datasets, a cycle-consistent architecture is devised with coupled
diffusive and non-diffusive modules that bilaterally translate between two
modalities. Extensive assessments are reported on the utility of SynDiff
against competing GAN and diffusion models in multi-contrast MRI and MRI-CT
translation. Our demonstrations indicate that SynDiff offers quantitatively and
qualitatively superior performance against competing baselines.Comment: M. Ozbey and O. Dalmaz contributed equally to this stud
Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
Recent years have witnessed a surge in deep generative models for accelerated
MRI reconstruction. Diffusion priors in particular have gained traction with
their superior representational fidelity and diversity. Instead of the target
transformation from undersampled to fully-sampled data, common diffusion priors
are trained to learn a multi-step transformation from Gaussian noise onto
fully-sampled data. During inference, data-fidelity projections are injected in
between reverse diffusion steps to reach a compromise solution within the span
of both the diffusion prior and the imaging operator. Unfortunately, suboptimal
solutions can arise as the normality assumption of the diffusion prior causes
divergence between learned and target transformations. To address this
limitation, here we introduce the first diffusion bridge for accelerated MRI
reconstruction. The proposed Fourier-constrained diffusion bridge (FDB)
leverages a generalized process to transform between undersampled and
fully-sampled data via random noise addition and random frequency removal as
degradation operators. Unlike common diffusion priors that use an asymptotic
endpoint based on Gaussian noise, FDB captures a transformation between finite
endpoints where the initial endpoint is based on moderate degradation of
fully-sampled data. Demonstrations on brain MRI indicate that FDB outperforms
state-of-the-art reconstruction methods including conventional diffusion
priors
Land degradation and natural environment in the western Anatolia
The study area is located in the lower part of the Aegean region which is part of the Western Anatolia. The area has appropriate natural environmental conditions (such as climate, topography, soil, water, natural harbor, transportation facilities, etc.). Due to the suitable conditions, there are intensive population and variety economic activities. The settlement started in 2,000 BC with Lydian, Ionians (Ephesus, Miletus, Pergamum, etc.) and continued with Roman, Seljuk and Ottomans. These can be seen in the several places in the research area. Due to the suitable land for agriculture, water sources and forest land, transportation facilities (land and sea transport availability), western Anatolia has been seen continuously under the human pressure
Denizli Çürüksu Havzası'nın hava kirliliği örneğinde arazi kullanım durumu ve çevre bilinci açısından önemi
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