Unidcp: Unifying multiple medical vision-language tasks via dynamic cross-modal learnable prompts
Medical vision-language pre-training (Med-VLP) models have recently accelerated the fast-
growing medical diagnostics application. However, most Med-VLP models learn task …
growing medical diagnostics application. However, most Med-VLP models learn task …
Multi-Scale Contourlet Knowledge Guide Learning Segmentation
For accurate segmentation, effective feature extraction has always been a challenging
problem, since the variability of appearance and the fuzziness of object boundaries …
problem, since the variability of appearance and the fuzziness of object boundaries …
Image Fusion via Vision-Language Model
Image fusion integrates essential information from multiple source images into a single
composite, emphasizing the highlighting structure and textures, and refining imperfect areas …
composite, emphasizing the highlighting structure and textures, and refining imperfect areas …
SIMFusion: A semantic information-guided modality-specific fusion network for MR Images
X Zhang, A Liu, G Yang, Y Liu, X Chen - Information Fusion, 2024 - Elsevier
Multi-modal medical image fusion aims to integrate distinct imaging modalities to yield more
comprehensive and precise medical images, which can benefit the subsequent image …
comprehensive and precise medical images, which can benefit the subsequent image …
Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification
S Iqbal, AN Qureshi, M Alhussein… - Frontiers in …, 2024 - frontiersin.org
The classification of medical images is crucial in the biomedical field, and despite attempts
to address the issue, significant challenges persist. To effectively categorize medical …
to address the issue, significant challenges persist. To effectively categorize medical …
DLGAN: Undersampled MRI reconstruction using Deep Learning based Generative Adversarial Network
Abstract Magnetic Resonance Imaging (MRI) is a crucial tool for quantitative image analysis
and clinical diagnosis, providing detailed anatomical images to assist in the detection of …
and clinical diagnosis, providing detailed anatomical images to assist in the detection of …
Cross-domain Low-dose CT Image Denoising with Semantic Preservation and Noise Alignment
Deep learning (DL)-based Low-dose CT (LDCT) image denoising methods may face
domain shift problem, where data from different domains (ie, hospitals) may have similar …
domain shift problem, where data from different domains (ie, hospitals) may have similar …
A dual-branch feature fusion neural network for fish image fine-grained recognition
X Geng, J Gao, Y Zhang, R Wang - The Visual Computer, 2024 - Springer
The recognition of fish species holds significant importance in aquaculture and marine
biology. However, it is a challenging problem due to the high similarity among intra-genus …
biology. However, it is a challenging problem due to the high similarity among intra-genus …
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
Image fusion aims to combine information from multiple source images into a single and
more informative image. A major challenge for deep learning-based image fusion algorithms …
more informative image. A major challenge for deep learning-based image fusion algorithms …
A Semantic-Aware and Multi-Guided Network for Infrared-Visible Image Fusion
X Zhang, L Wang, L Zhao, X Li, S Ma - arXiv preprint arXiv:2407.06159, 2024 - arxiv.org
Multi-modality image fusion aims at fusing specific-modality and shared-modality information
from two source images. To tackle the problem of insufficient feature extraction and lack of …
from two source images. To tackle the problem of insufficient feature extraction and lack of …