Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Healthcare data are inherently multimodal, including electronic health records (EHR),
medical images, and multi-omics data. Combining these multimodal data sources …
medical images, and multi-omics data. Combining these multimodal data sources …
Prompt engineering for healthcare: Methodologies and applications
Prompt engineering is a critical technique in the field of natural language processing that
involves designing and optimizing the prompts used to input information into models, aiming …
involves designing and optimizing the prompts used to input information into models, aiming …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
BrainNet: optimal deep learning feature fusion for brain tumor classification
Early detection of brain tumors can save precious human life. This work presents a fully
automated design to classify brain tumors. The proposed scheme employs optimal deep …
automated design to classify brain tumors. The proposed scheme employs optimal deep …
LCDAE: data augmented ensemble framework for lung cancer classification
Objective: The only possible solution to increase the patients' fatality rate is lung cancer
early-stage detection. Recently, deep learning techniques became the most promising …
early-stage detection. Recently, deep learning techniques became the most promising …
Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound
Z Xu, Y Wang, M Chen, Q Zhang - Computers in Biology and Medicine, 2022 - Elsevier
Purpose The ultrasound (US) diagnosis of breast cancer is usually based on a single-region
of a whole breast tumor from a single ultrasonic modality, which limits the diagnostic …
of a whole breast tumor from a single ultrasonic modality, which limits the diagnostic …
M4fnet: Multimodal medical image fusion network via multi-receptive-field and multi-scale feature integration
The main purpose of multimodal medical image fusion is to aggregate the significant
information from different modalities and obtain an informative image, which provides …
information from different modalities and obtain an informative image, which provides …
[HTML][HTML] External multi-modal imaging sensor calibration for sensor fusion: A review
Multi-modal data fusion has gained popularity due to its diverse applications, leading to an
increased demand for external sensor calibration. Despite several proven calibration …
increased demand for external sensor calibration. Despite several proven calibration …
Explainable deep-learning-based diagnosis of Alzheimer's disease using multimodal input fusion of PET and MRI Images
Purpose Alzheimer's disease (AD) is a progressive, incurable human brain illness that
impairs reasoning and retention as well as recall. Detecting AD in its preliminary stages …
impairs reasoning and retention as well as recall. Detecting AD in its preliminary stages …