IEEE journal of biomedical and health informatics
Dec 5, 2024
Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient labeled data to train the models. There are many models in t...
IEEE journal of biomedical and health informatics
Dec 5, 2024
Graph Neural Networks (GNNs) play a pivotal role in learning representations of brain networks for estimating brain age. However, the over-squashing impedes interactions between long-range nodes, hindering the ability of message-passing mechanism-bas...
IEEE journal of biomedical and health informatics
Dec 5, 2024
Time-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for the non-invasive characterization of tissue microstructure. These models require densely sampled q-t space data for microstructural fitting, leading to very time-consuming acq...
The classification of land cover objects in hyperspectral imagery (HSI) has significantly advanced due to the development of convolutional neural networks (CNNs). However, challenges such as limited training data and high dimensionality negatively im...
Cerebrovascular segmentation is a crucial preliminary task for many computer-aided diagnosis tools dealing with cerebrovascular pathologies. Over the last years, deep learning based methods have been widely applied to this task. However, classic deep...
Neural networks : the official journal of the International Neural Network Society
Dec 4, 2024
Self-supervised learning for image denoising problems in the presence of denaturation for noisy data is a crucial approach in machine learning. However, theoretical understanding of the performance of the approach that uses denatured data is lacking....
Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In tu...
IEEE transactions on pattern analysis and machine intelligence
Dec 4, 2024
Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images...
Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human ...
Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the ...
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