AIMC Topic: Image Processing, Computer-Assisted

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Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis.

Current medical imaging
BACKGROUND: Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in term...

CvTMorph: Improving Local Feature Extraction in Medical Image Registration for Respiratory Motion Modeling with Convolutional Vision Transformer.

Current medical imaging
BACKGROUND: Accurately modeling respiratory motion in medical images is crucial for various applications, including radiation therapy planning. However, existing registration methods often struggle to extract local features effectively, limiting thei...

[Modern opportunities of using computer programs and mobile devices in the frame of personality identification].

Sudebno-meditsinskaia ekspertiza
A technology of mobile devices on the basis of Android and iOS sharing, in which previously trained neural networks on the mobile device with the use of the Skull-face program place the reference points in automatic mode with subsequent analysis of t...

Enhancing Organizing Pneumonia Diagnosis: A Novel Super-token Transformer Approach for Masson Body Segmentation.

In vivo (Athens, Greece)
BACKGROUND/AIM: In this study, we introduce an innovative deep-learning model architecture aimed at enhancing the accuracy of detecting and classifying organizing pneumonia (OP), a condition characterized by the presence of Masson bodies within the a...

Multi-disease X-ray Image Classification of the Chest Based on Global and Local Fusion Adaptive Networks.

Current medical imaging
BACKGROUND: Chest X-ray image classification for multiple diseases is an important research direction in the field of computer vision and medical image processing. It aims to utilize advanced image processing techniques and deep learning algorithms t...

DNeuroMAT: A Deep-Learning-Based Neuron Morphology Analysis Toolbox.

Methods in molecular biology (Clifton, N.J.)
Digital reconstruction of neuronal structures from 3D neuron microscopy images is critical for the quantitative investigation of brain circuits and functions. Currently, neuron reconstructions are mainly obtained by manual or semiautomatic methods. H...

Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important at...

Multi-dimensional dense attention network for pixel-wise segmentation of optic disc in colour fundus images.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Segmentation of retinal fragments like blood vessels, Optic Disc (OD), and Optic Cup (OC) enables the early detection of different retinal pathologies like Diabetic Retinopathy (DR), Glaucoma, etc.

Alzheimer's Disease Prediction Using Fly-Optimized Densely Connected Convolution Neural Networks Based on MRI Images.

The journal of prevention of Alzheimer's disease
Alzheimer's is a degenerative brain cell disease that affects around 5.8 million people globally. The progressive neurodegenerative disease known as Alzheimer's Disease (AD), affects the frontal cortex, the part of the brain in charge of memory, lang...