Search
Search Results
-
Medical Image Encryption using Biometric Image Texture Fusion
In conjunction with pandemics, medical image data are growing exponentially. In some countries, hospitals collect biometric data from patients, such...
-
From CNN to Transformer: A Review of Medical Image Segmentation Models
Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and...
-
A deformable patch-based transformer for 3D medical image registration
PurposeMedical image registration is of great importance in clinical medicine. However, medical image registration algorithms are still in the...
-
DeepPyramid+: medical image segmentation using Pyramid View Fusion and Deformable Pyramid Reception
PurposeSemantic segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural...
-
Invariant Content Representation for Generalizable Medical Image Segmentation
Domain generalization (DG) for medical image segmentation due to privacy preservation prefers learning from a single-source domain and expects good...
-
Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer
With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided...
-
Using diffusion models to generate synthetic labeled data for medical image segmentation
PurposeMedical image analysis has become a prominent area where machine learning has been applied. However, high-quality, publicly available data are...
-
SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules
BackgroundMedical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are...
-
Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification—Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline
De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for...
-
MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network
PurposeThis study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT)...
-
Medical image diagnosis based on adaptive Hybrid Quantum CNN
Hybrid quantum systems have shown promise in image classification by combining the strengths of both classical and quantum algorithms. These systems...
-
GA-UNet: A Lightweight Ghost and Attention U-Net for Medical Image Segmentation
U-Net has demonstrated strong performance in the field of medical image segmentation and has been adapted into various variants to cater to a wide...
-
SGSR: style-subnets-assisted generative latent bank for large-factor super-resolution with registered medical image dataset
PurposeWe propose a large-factor super-resolution (SR) method for performing SR on registered medical image datasets. Conventional SR approaches use...
-
Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and...
-
MBUTransNet: multi-branch U-shaped network fusion transformer architecture for medical image segmentation
PurposeRecently, transformers have been adopted to computer vision applications and achieve great success in image segmentation. However by simply...
-
MedYOLO: A Medical Image Object Detection Framework
Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional...
-
Optimal Deep CNN–Based Vectorial Variation Filter for Medical Image Denoising
Medical imaging has acquired more attention due to the emerging design of wireless technologies, the internet, and data storage. The reflection of...
-
The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation
As applications of AI in medicine continue to expand, there is an increasing focus on integration into clinical practice. An underappreciated aspect...
-
TBUnet: A Pure Convolutional U-Net Capable of Multifaceted Feature Extraction for Medical Image Segmentation
Many current medical image segmentation methods utilize convolutional neural networks (CNNs), with some extended U-Net-based networks relying on deep...
-
Robust co-teaching learning with consistency-based noisy label correction for medical image classification
PurposeDeep neural networks (DNNs) have made great achievements in computer-aided diagnostic systems, but the success highly depends on massive data...
