A holistic overview of deep learning approach in medical imaging
Medical images are a rich source of invaluable necessary information used by clinicians.
Recent technologies have introduced many advancements for exploiting the most of this …
Recent technologies have introduced many advancements for exploiting the most of this …
Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review
The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare,
addressing critical challenges in securing electronic health records (EHRs), ensuring data …
addressing critical challenges in securing electronic health records (EHRs), ensuring data …
Bidirectional mapping generative adversarial networks for brain MR to PET synthesis
Fusing multi-modality medical images, such as magnetic resonance (MR) imaging and
positron emission tomography (PET), can provide various anatomical and functional …
positron emission tomography (PET), can provide various anatomical and functional …
BPGAN: Brain PET synthesis from MRI using generative adversarial network for multi-modal Alzheimer's disease diagnosis
Abstract Background and Objective Multi-modal medical images, such as magnetic
resonance imaging (MRI) and positron emission tomography (PET), have been widely used …
resonance imaging (MRI) and positron emission tomography (PET), have been widely used …
Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks
Lowering either the administered activity or scan time is desirable in PET imaging as it
decreases the patient's radiation burden or improves patient comfort and reduces motion …
decreases the patient's radiation burden or improves patient comfort and reduces motion …
[HTML][HTML] ResNet 及其在医学图像处理领域的应用: 研究进展与挑战
周涛， 刘赟璨， 陆惠玲， 叶鑫宇， 常晓玉 - 电子与信息学报, 2022 - jeit.ac.cn
残差神经网络(ResNet) 是深度学习领域的研究热点, 广泛应用于医学图像处理领域.
该文对残差神经网络从以下几个方面进行综述: 首先, 阐述残差神经网络的基本原理和模型结构; …
该文对残差神经网络从以下几个方面进行综述: 首先, 阐述残差神经网络的基本原理和模型结构; …
A feature transfer enabled multi-task deep learning model on medical imaging
Object detection, segmentation, and classification are three common tasks in medical image
analysis. Multi-task deep learning (MTL) tackles these three tasks jointly, which provides two …
analysis. Multi-task deep learning (MTL) tackles these three tasks jointly, which provides two …
Medprompt: Cross-modal prompting for multi-task medical image translation
The ability to translate medical images across different modalities is crucial for synthesizing
missing data and aiding in clinical diagnosis. However, existing learning-based techniques …
missing data and aiding in clinical diagnosis. However, existing learning-based techniques …
Learning optimized low-light image enhancement for edge vision tasks
SM A Sharif, A Myrzabekov… - Proceedings of the …, 2024 - openaccess.thecvf.com
Low-light image enhancement (LLIE) has a significant role in edge vision applications
(EVA). Despite its widespread practicability the existing LLIE methods are impractical due to …
(EVA). Despite its widespread practicability the existing LLIE methods are impractical due to …
AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction
Abstract The prediction of Mild Cognitive Impairment (MCI) patients who are at higher risk
converting to Alzheimer's Disease (AD) is critical for effective intervention and patient …
converting to Alzheimer's Disease (AD) is critical for effective intervention and patient …