Machine learning for medical imaging: methodological failures and recommendations for the future
G Varoquaux, V Cheplygina - NPJ digital medicine, 2022 - nature.com
Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …
health. However, a number of systematic challenges are slowing down the progress of the …
Brain imaging-based machine learning in autism spectrum disorder: methods and applications
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood
onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is …
onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is …
Hybrid embedding-based text representation for hierarchical multi-label text classification
Many real-world text classification tasks often deal with a large number of closely related
categories organized in a hierarchical structure or taxonomy. Hierarchical multi-label text …
categories organized in a hierarchical structure or taxonomy. Hierarchical multi-label text …
Machine learning and modeling: data, validation, communication challenges
With the era of big data, the utilization of machine learning algorithms in radiation oncology
is rapidly growing with applications including: treatment response modeling, treatment …
is rapidly growing with applications including: treatment response modeling, treatment …
A novel CNN architecture for accurate early detection and classification of Alzheimer's disease using MRI data
Alzheimer's disease (AD) is a debilitating neurodegenerative disorder that requires accurate
diagnosis for effective management and treatment. In this article, we propose an architecture …
diagnosis for effective management and treatment. In this article, we propose an architecture …
Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach
The brain electrical activity, recorded and materialized as electroencephalogram (EEG)
signals, is known to be very useful in the diagnosis of brain-related pathology. However …
signals, is known to be very useful in the diagnosis of brain-related pathology. However …
Machine learning framework with feature selection approaches for thyroid disease classification and associated risk factors identification
A Sultana, R Islam - Journal of Electrical Systems and Information …, 2023 - Springer
Thyroid disease (TD) develops when the thyroid does not generate an adequate quantity of
thyroid hormones as well as when a lump or nodule emerges due to aberrant growth of the …
thyroid hormones as well as when a lump or nodule emerges due to aberrant growth of the …
Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review
V Balakrishnan, Y Kherabi, G Ramanathan… - Progress in biophysics …, 2023 - Elsevier
Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment
initiation, and thus improve outcomes. This review synthesizes the literature on biomarker …
initiation, and thus improve outcomes. This review synthesizes the literature on biomarker …
Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study
A critical problem that Emergency Departments (EDs) must address is overcrowding, as it
causes extended waiting times and increased patient dissatisfaction, both of which are …
causes extended waiting times and increased patient dissatisfaction, both of which are …
HDFCN: A robust hybrid deep network based on feature concatenation for cervical cancer diagnosis on WSI pap smear slides
Cervical cancer is a critical imperilment to a female's health due to its malignancy and fatality
rate. The disease can be thoroughly cured by locating and treating the infected tissues in the …
rate. The disease can be thoroughly cured by locating and treating the infected tissues in the …