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The widespread adoption of social media has heightened interest in its psychological effects, particularly on mental health indicators such as anxiety, depression, loneliness, and sleep quality, as these platforms increasingly influence…
The application of machine learning algorithms to the diagnosis and analysis of Alzheimer's disease (AD) from multimodal neuroimaging data is a current research hotspot. It remains a formidable challenge to learn brain region information…
Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive nature of these cases, doctors are required to propose a correct…
Coronary artery disease remains one of the leading causes of mortality globally. Despite advances in revascularization treatments like PCI and CABG, postoperative stroke is inevitable. This study aims to develop and evaluate a sophisticated…
Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are…
Predicting brain maturity using noninvasive magnetic resonance images (MRI) can distinguish different age groups and help to assess neurodevelopmental disorders. However, group-wise differences are often less informative for assessing…
Effective and accurate diagnosis of Alzheimer's disease (AD) or mild cognitive impairment (MCI) can be critical for early treatment and thus has attracted more and more attention nowadays. Since first introduced, machine learning methods…
Purpose: There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images can be utilised for attenuation correction, patient positioning, and dose planning in…
Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning algorithms have been shown to be an…
Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely encountered in neuroimaging studies. To avoid over-fitting and…
Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses. However, observing and quantifying associations in images can be difficult because of the wide variety of…
A precise assessment of the risk of breast lesions can greatly lower it and assist physicians in choosing the best course of action. To categorise breast lesions, the majority of current computer-aided systems only use characteristics from…
We suggest that deep learning can be used for pre-screening cancer by analyzing demographic and anthropometric information of patients, as well as biological markers obtained from routine blood samples and relative risks obtained from…
Stroke remains one of the most critical global health challenges, ranking as the second leading cause of death and the third leading cause of disability worldwide. This study explores the effectiveness of machine learning algorithms in…
Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk,…
While deep-learning based recommender systems utilizing collaborative filtering have been commonly used for recommendation in other domains, their application in the medical domain have been limited. In addition to modeling user-item…
Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. However, current tools including genomic assays lack the accuracy required for optimal clinical decision-making. We developed a novel…
Brain tumors are among the deadliest diseases in the world. Magnetic Resonance Imaging (MRI) is one of the most effective ways to detect brain tumors. Accurate detection of brain tumors based on MRI scans is critical, as it can potentially…
In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in…
Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays…