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Federated learning is an emerging technology that enables the decentralised training of machine learning-based methods for medical image analysis across multiple sites while ensuring privacy. This review paper thoroughly examines federated learning research applied to medical image analysis, outlining technical contributions. We followed the guidelines of Okali and Schabram, a review methodology, to produce a comprehensive summary and discussion of the literature in information systems. Searches were conducted at leading indexing platforms: PubMed, IEEE Xplore, Scopus, ACM, and Web of Science. We found a total of 433 papers and selected 118 of them for further examination. The findings highlighted research on applying federated learning to neural network methods in cardiology, dermatology, gastroenterology, neurology, oncology, respiratory medicine, and urology. The main challenges reported were the ability of machine learning models to adapt effectively to real-world datasets and privacy preservation. We outlined two strategies to address these challenges: non-independent and identically distributed data and privacy-enhancing methods. This review paper offers a reference overview for those already working in the field and an introduction to those new to the topic. [ABSTRACT FROM AUTHOR]

Federated learning is an emerging technology that enables the decentralised training of machine learning-based methods for medical image analysis across multiple sites while ensuring privacy. This review paper thoroughly examines federated learning research applied to medical image analysis, outlining technical contributions. We followed the guidelines of Okali and Schabram, a review methodology, to produce a comprehensive summary and discussion of the literature in information systems. Searches were conducted at leading indexing platforms: PubMed, IEEE Xplore, Scopus, ACM, and Web of Science. We found a total of 433 papers and selected 118 of them for further examination. The findings highlighted research on applying federated learning to neural network methods in cardiology, dermatology, gastroenterology, neurology, oncology, respiratory medicine, and urology. The main challenges reported were the ability of machine learning models to adapt effectively to real-world datasets and privacy preservation. We outlined two strategies to address these challenges: non-independent and identically distributed data and privacy-enhancing methods. This review paper offers a reference overview for those already working in the field and an introduction to those new to the topic.

This paper intends to present a comparative review of functional connectivity (FC) analysis methods and their computational methodologies measured through functional magnetic resonance imaging (fMRI). The fMRI technique has been established as a powerful tool for identifying and visualizing the active brain areas in response to certain stimuli and tasks. FC is a metric for the interaction between various brain regions. The synchronization of the functional activity between non-adjacent brain regions is reflected in FC, and changes in FC occur earlier than changes in the physical brain structure. The functionally active brain area can be identified by detecting signal changes caused by blood oxygen levels during the corresponding neuronal activity. The fMRI technique can assess these physiological signals, which can be utilized for further study and research. FC is therefore crucial in identifying a variety of brain disorders, including Alzheimer's (AD). AD is a neurodegenerative disease that primarily affects the elderly, and previous studies have reported that patients with AD seem to have impaired FC between different brain areas. Henceforth, AD patients' clinical diagnosis and prediction depend significantly on the practical and precise classification of symptoms using fMRI. We have first reviewed the existing FC analysis methods, such as model/seed-based methods and data-driven methods, and further compared them based on the reduced FC observed in AD patients compared to normal controls (NC). The goal is to provide an overview of the benefits, challenges, and limitations of FC analysis methods in the context of medical imaging for AD. [ABSTRACT FROM AUTHOR]

This paper intends to present a comparative review of functional connectivity (FC) analysis methods and their computational methodologies measured through functional magnetic resonance imaging (fMRI). The fMRI technique has been established as a powerful tool for identifying and visualizing the active brain areas in response to certain stimuli and tasks. FC is a metric for the interaction between various brain regions. The synchronization of the functional activity between non-adjacent brain regions is reflected in FC, and changes in FC occur earlier than changes in the physical brain structure. The functionally active brain area can be identified by detecting signal changes caused by blood oxygen levels during the corresponding neuronal activity. The fMRI technique can assess these physiological signals, which can be utilized for further study and research. FC is therefore crucial in identifying a variety of brain disorders, including Alzheimer’s (AD). AD is a neurodegenerative disease that primarily affects the elderly, and previous studies have reported that patients with AD seem to have impaired FC between different brain areas. Henceforth, AD patients’ clinical diagnosis and prediction depend significantly on the practical and precise classification of symptoms using fMRI. We have first reviewed the existing FC analysis methods, such as model/seed-based methods and data-driven methods, and further compared them based on the reduced FC observed in AD patients compared to normal controls (NC). The goal is to provide an overview of the benefits, challenges, and limitations of FC analysis methods in the context of medical imaging for AD.

Background: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. Methods: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. Results: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.

Objective In adulthood, the diagnosis of attention-deficit/hyperactivity disorder ( ADHD) has been subject of recent controversy. We searched for a neuroanatomical signature associated with ADHD spectrum symptoms in adults by applying, for the first time, machine learning-based pattern classification methods to structural MRI and diffusion tensor imaging ( DTI) data obtained from stimulant-naïve adults with childhood-onset ADHD and healthy controls ( HC). Method Sixty-seven ADHD patients and 66 HC underwent high-resolution T1-weighted and DTI acquisitions. A support vector machine ( SVM) classifier with a non-linear kernel was applied on multimodal image features extracted on regions of interest placed across the whole brain. Results The discrimination between a mixed-gender ADHD subgroup and individually matched HC ( n = 58 each) yielded area-under-the-curve ( AUC) and diagnostic accuracy ( DA) values of up to 0.71% and 66% ( P = 0.003) respectively. AUC and DA values increased to 0.74% and 74% ( P = 0.0001) when analyses were restricted to males (52 ADHD vs. 44 HC). Conclusion Introvert personality traits showed independent risk effects on suicidality regardless of diagnosis status. Among high risk individuals with suicidal thoughts, higher neuroticism tendency is further associated with increased risk of suicide attempt. [ABSTRACT FROM AUTHOR]

Background: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. Methods: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. Results: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions. [ABSTRACT FROM AUTHOR]