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Physical Sciences and Mathematics Commons™
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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …
Semi-Supervised Adversarial Domain Adaptation For Seagrass Detection Using Multispectral Images In Coastal Areas, Kazi Aminul Islam, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li
Semi-Supervised Adversarial Domain Adaptation For Seagrass Detection Using Multispectral Images In Coastal Areas, Kazi Aminul Islam, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li
Electrical & Computer Engineering Faculty Publications
Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from …
Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li
Flood Detection Using Multi-Modal And Multi-Temporal Images: A Comparative Study, Kazi Aminul Islam, Mohammad Shahab Uddin, Chiman Kwan, Jiang Li
Electrical & Computer Engineering Faculty Publications
Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that …