AIMC Topic: Image Processing, Computer-Assisted

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TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion.

Sensors (Basel, Switzerland)
As deep learning technology has progressed, automated medical image analysis is becoming ever more crucial in clinical diagnosis. However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in bloo...

3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer's disease detection using structural MRI.

Biomedical physics & engineering express
Alzheimer's disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. This leads to cognitive decline and impaired social functioning, making it a major contributor to dem...

SELFNet: Denoising Shear Wave Elastography Using Spatial-temporal Fourier Feature Networks.

Ultrasound in medicine & biology
OBJECTIVE: Ultrasound-based shear wave elastography offers estimation of tissue stiffness through analysis of the propagation of a shear wave induced by a stimulus. Displacement or velocity fields during the process can contain noise as a result of t...

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems.

International journal of neural systems
Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image seg...

A flexible 2.5D medical image segmentation approach with in-slice and cross-slice attention.

Computers in biology and medicine
Deep learning has become the de facto method for medical image segmentation, with 3D segmentation models excelling in capturing complex 3D structures and 2D models offering high computational efficiency. However, segmenting 2.5D images, characterized...

Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification.

F1000Research
Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monitoring digestive tract diseases. However, the analysis of images obtained from wireless capsule endoscopy is a challenging task, as the images are of lo...

Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging.

NeuroImage
Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feas...

Dynamic MRI interpolation in temporal direction using an unsupervised generative model.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
PURPOSE: Cardiac cine magnetic resonance imaging (MRI) is an important tool in assessing dynamic heart function. However, this technique requires long acquisition time and long breath holds, which presents difficulties. The aim of this study is to pr...

MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration.

Medical image analysis
Deep-learning-based deformable image registration (DL-DIR) has demonstrated improved accuracy compared to time-consuming non-DL methods across various anatomical sites. However, DL-DIR is still challenging in heterogeneous tissue regions with large d...