[HTML][HTML] Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …
grows, especially in high-stakes decision making areas such as medical image analysis …
Advances in data preprocessing for biomedical data fusion: An overview of the methods, challenges, and prospects
Due to the proliferation of biomedical imaging modalities, such as Photoacoustic
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc …
Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …
explainability, attempts to reveal the working mechanisms of complex models. From a …
Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review
Abstract Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and
symptoms that appear in early childhood. ASD is also associated with communication …
symptoms that appear in early childhood. ASD is also associated with communication …
Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach
VS Diogo, HA Ferreira, D Prata… - Alzheimer's Research & …, 2022 - Springer
Background Early and accurate diagnosis of Alzheimer's disease (AD) is essential for
disease management and therapeutic choices that can delay disease progression. Machine …
disease management and therapeutic choices that can delay disease progression. Machine …
Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks
In this study, an automatic autism diagnostic model based on sMRI is proposed. This
proposed model consists of two basic stages. The first stage is the preprocessing stage …
proposed model consists of two basic stages. The first stage is the preprocessing stage …
Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data
Many machine learning-based studies have been carried out in the literature for the
detection of brain tumors using MRI data and most of what has been done in the last 6 years …
detection of brain tumors using MRI data and most of what has been done in the last 6 years …
Pre-trained deep learning models for brain MRI image classification
S Krishnapriya, Y Karuna - Frontiers in Human Neuroscience, 2023 - frontiersin.org
Brain tumors are serious conditions caused by uncontrolled and abnormal cell division.
Tumors can have devastating implications if not accurately and promptly detected. Magnetic …
Tumors can have devastating implications if not accurately and promptly detected. Magnetic …
Transfer learning for Alzheimer's disease through neuroimaging biomarkers: a systematic review
Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since
2017, deep learning models with transfer learning approaches have been gaining …
2017, deep learning models with transfer learning approaches have been gaining …