[HTML][HTML] Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames
Brain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-
invasive method that provides multi-modal images containing important information …
invasive method that provides multi-modal images containing important information …
Reliable mutual distillation for medical image segmentation under imperfect annotations
Convolutional neural networks (CNNs) have made enormous progress in medical image
segmentation. The learning of CNNs is dependent on a large amount of training data with …
segmentation. The learning of CNNs is dependent on a large amount of training data with …
Delve into Multiple Sclerosis (MS) lesion exploration: A modified attention U-Net for MS lesion segmentation in Brain MRI
Multiple Sclerosis (MS) is a Central Nervous System (CNS) disease that Magnetic
Resonance Imaging (MRI) system can detect and segment its lesions. Artificial Neural …
Resonance Imaging (MRI) system can detect and segment its lesions. Artificial Neural …
Digital infrared thermal imaging system based breast cancer diagnosis using 4D U-Net segmentation
Medical Research field has been taken continuous efforts to develop an efficient method for
detecting breast cancer, but the goal has still not yet achieved. To overcome this issue, a 4D …
detecting breast cancer, but the goal has still not yet achieved. To overcome this issue, a 4D …
Multiple sclerosis lesion analysis in brain magnetic resonance images: techniques and clinical applications
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central
nervous system, characterized by the appearance of focal lesions in the white and gray …
nervous system, characterized by the appearance of focal lesions in the white and gray …
Image preprocessing with contrast-limited adaptive histogram equalization improves the segmentation performance of deep learning for the articular disk of the …
Y Yoshimi, Y Mine, S Ito, S Takeda, S Okazaki… - Oral Surgery, Oral …, 2024 - Elsevier
Objectives The objective was to evaluate the robustness of deep learning (DL)-based
encoder–decoder convolutional neural networks (ED-CNNs) for segmenting …
encoder–decoder convolutional neural networks (ED-CNNs) for segmenting …
Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation
Accurate and reliable measures of cortical thickness from magnetic resonance imaging are
an important biomarker to study neurodegenerative and neurological disorders …
an important biomarker to study neurodegenerative and neurological disorders …
Applicable artificial intelligence for brain disease: A survey
Brain diseases threaten hundreds of thousands of people over the world. Medical imaging
techniques such as MRI and CT are employed for various brain disease studies. As artificial …
techniques such as MRI and CT are employed for various brain disease studies. As artificial …