Browsing by Subject "Heterogeneity"

Now showing 1 - 4 of 4

Open Access
Detection of compound structures using a gaussian mixture model with spectral and spatial constraints
(Institute of Electrical and Electronics Engineers Inc., 2014) Arı, C.; Aksoy, S.
Increasing spectral and spatial resolution of new-generation remotely sensed images necessitate the joint use of both types of information for detection and classification tasks. This paper describes a new approach for detecting heterogeneous compound structures such as different types of residential, agricultural, commercial, and industrial areas that are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. The proposed approach uses Gaussian mixture models (GMMs), in which the individual Gaussian components model the spectral and shape characteristics of the individual primitives and an associated layout model is used to model their spatial arrangements. We propose a novel expectation-maximization (EM) algorithm that solves the detection problem using constrained optimization. The input is an example structure of interest that is used to estimate a reference GMM and construct spectral and spatial constraints. Then, the EM algorithm fits a new GMM to the target image data so that the pixels with high likelihoods of being similar to the Gaussian object models while satisfying the spatial layout constraints are identified without any requirement for region segmentation. Experiments using WorldView-2 images show that the proposed method can detect high-level structures that cannot be modeled using traditional techniques. © 1980-2012 IEEE.
Embargo
One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis
(ELSEVIER, 2024-05) Dalmaz, Onat; Mirza, Muhammad Usama; Elmas, Gökberk; Özbey, Muzaffer; Dar, Salman UI Hassan; Ceyani, Emir; Karlı Oğuz, Kader; Avestimehr, Salman; Çukur, Tolga
Curation of large, diverse MRI datasets via multi-institutional collaborations can help improve learningof generalizable synthesis models that reliably translate source- onto target-contrast images. To facilitatecollaborations, federated learning (FL) adopts decentralized model training while mitigating privacy concernsby avoiding sharing of imaging data. However, conventional FL methods can be impaired by the inherentheterogeneity in the data distribution, with domain shifts evident within and across imaging sites. Here weintroduce the first personalized FL method for MRI Synthesis (pFLSynth) that improves reliability against dataheterogeneity via model specialization to individual sites and synthesis tasks (i.e., source-target contrasts).To do this, pFLSynth leverages an adversarial model equipped with novel personalization blocks that controlthe statistics of generated feature maps across the spatial/channel dimensions, given latent variables specificto sites and tasks. To further promote communication efficiency and site specialization, partial networkaggregation is employed over later generator stages while earlier generator stages and the discriminatorare trained locally. As such, pFLSynth enables multi-task training of multi-site synthesis models with highgeneralization performance across sites and tasks. Comprehensive experiments demonstrate the superiorperformance and reliability of pFLSynth in MRI synthesis against prior federated methods
Open Access
Protein folding rates correlate with heterogeneity of folding mechanism
(American Physical Society, 2004) Öztop, B.; Ejtehadi, M. R.; Plotkin, S. S.
The folding rates of protein were shown to correlate with the degree of heterogeneity in the formation of native contacts. It was shown that both experimental rates and simulated free energy barriers for 2-state proteins depend on the degree of heterogeneity present in the folding process. Heterogeneity due to variance in the distribution of native loop lengths, and variance in the distribution of φ values, were observed to increase folding rates and reduce folding barriers. The observed effect due to φ variance was found to be the most statistically significant, because φ variance captures both heterogeneity arising from native topology and that arising from energetics.
Open Access
A specificity-preserving generative model for federated MRI translation
(Springer Cham, 2022-10-07) Dalmaz, Onat; Mirza, Usama; Elmas, Gökberk; Özbey, Muzaffer; Dar, Salman U. H; Çukur, Tolga; Albarqouni, Shadi; Bakas, Spyridon; Bano, Sophia; Cardoso, M. Jorge; Khanal, Bishesh; Landman, Bennett; Li, Xiaoxiao
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 learning (FL) has emerged as a collaboration framework that enables decentralized training to avoid sharing of imaging data. However, FL-trained translation models can deteriorate by the inherent heterogeneity in the distribution of MRI data. To improve reliability against domain shifts, here we introduce a novel specificity-preserving FL method for MRI contrast translation. The proposed approach is based on an adversarial model that adaptively normalizes the feature maps across the generator based on site-specific latent variables. Comprehensive FL experiments were conducted on multi-site datasets to show the effectiveness of the proposed approach against prior federated methods in MRI contrast translation.