Data-centric foundation models in computational healthcare: A survey
The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a
wave of opportunities in computational healthcare. The interactive nature of these models …
wave of opportunities in computational healthcare. The interactive nature of these models …
Transformer based conditional GAN for multimodal image fusion
Multimodal Image fusion is becoming urgent in multi-sensor information utilization. However,
existing end-to-end image fusion frameworks ignore a priori knowledge integration and long …
existing end-to-end image fusion frameworks ignore a priori knowledge integration and long …
[HTML][HTML] DDCNN-F: double decker convolutional neural network'F'feature fusion as a medical image classification framework
N Veeramani, P Jayaraman, R Krishankumar… - Scientific Reports, 2024 - nature.com
Melanoma is a severe skin cancer that involves abnormal cell development. This study aims
to provide a new feature fusion framework for melanoma classification that includes a novel …
to provide a new feature fusion framework for melanoma classification that includes a novel …
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 …
TGF: Multiscale transformer graph attention network for multi-sensor image fusion
Multisensor image fusion is a challenging task that aims to produce a composite image by
fusing visible (VI) and infrared (IR) images. Deep neural networks have shown impressive …
fusing visible (VI) and infrared (IR) images. Deep neural networks have shown impressive …
ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification
Abstract Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow
overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year …
overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year …
An efficient approach to medical image fusion based on optimization and transfer learning with VGG19
Medical image fusion is the process of combining information from multiple medical images
of the same body region acquired using different imaging modalities, such as computed …
of the same body region acquired using different imaging modalities, such as computed …
[HTML][HTML] Extraction of urban built-up area based on deep learning and multi-sources data fusion—The application of an emerging technology in urban planning
J Zhang, X Zhang, X Tan, X Yuan - Land, 2022 - mdpi.com
With the rapid expansion of urban built-up areas in recent years, it has become particularly
urgent to develop a fast, accurate and popularized urban built-up area extraction method …
urgent to develop a fast, accurate and popularized urban built-up area extraction method …
[HTML][HTML] Electroencephalogram-based motor imagery signals classification using a multi-branch convolutional neural network model with attention blocks
GA Altuwaijri, G Muhammad - Bioengineering, 2022 - mdpi.com
Brain signals can be captured via electroencephalogram (EEG) and be used in various
brain–computer interface (BCI) applications. Classifying motor imagery (MI) using EEG …
brain–computer interface (BCI) applications. Classifying motor imagery (MI) using EEG …
ADDNS: An asymmetric dual deep network with sharing mechanism for medical image fusion of CT and MR-T2
W Huang, H Zhang, H Guo, W Li, X Quan… - Computers in Biology and …, 2023 - Elsevier
Medical images with different modalities have different semantic characteristics. Medical
image fusion aiming to promotion of the visual quality and practical value has become …
image fusion aiming to promotion of the visual quality and practical value has become …