AIMC Topic: Image Processing, Computer-Assisted

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An hetero-modal deep learning framework for medical image synthesis applied to contrast and non-contrast MRI.

Biomedical physics & engineering express
Some pathologies such as cancer and dementia require multiple imaging modalities to fully diagnose and assess the extent of the disease. Magnetic resonance imaging offers this kind of polyvalence, but examinations take time and can require contrast a...

Illumination-aware divide-and-conquer network for improperly-exposed image enhancement.

Neural networks : the official journal of the International Neural Network Society
Improperly-exposed images often have unsatisfactory visual characteristics like inadequate illumination, low contrast, and the loss of small structures and details. The mapping relationship from an improperly-exposed condition to a well-exposed one m...

A deep learning phase-based solution in 2D echocardiography motion estimation.

Physical and engineering sciences in medicine
In this paper, we propose a new deep learning method based on Quaternion Wavelet Transform (QWT) phases of 2D echocardiographic sequences to estimate the motion and strain of myocardium. The proposed method considers intensity and phases gained from ...

radMLBench: A dataset collection for benchmarking in radiomics.

Computers in biology and medicine
BACKGROUND: New machine learning methods and techniques are frequently introduced in radiomics, but they are often tested on a single dataset, which makes it challenging to assess their true benefit. Currently, there is a lack of a larger, publicly a...

The value of CCTA combined with machine learning for predicting angina pectoris in the anomalous origin of the right coronary artery.

Biomedical engineering online
BACKGROUND: Anomalous origin of coronary artery is a common coronary artery anatomy anomaly. The anomalous origin of the coronary artery may lead to problems such as narrowing of the coronary arteries at the beginning of the coronary arteries and abn...

An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina.

Scientific reports
Diabetic retinopathy (DR), a leading cause of blindness in diabetic patients, necessitates the precise segmentation of lesions for the effective grading of lesions. DR multi-lesion segmentation faces the main concerns as follows. On the one hand, ret...

Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.

Journal of medical systems
In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive com...

STransU2Net: Transformer based hybrid model for building segmentation in detailed satellite imagery.

PloS one
As essential components of human society, buildings serve a multitude of functions and significance. Convolutional Neural Network (CNN) has made remarkable progress in the task of building extraction from detailed satellite imagery, owing to the pote...

A Novel Network for Low-Dose CT Denoising Based on Dual-Branch Structure and Multi-Scale Residual Attention.

Journal of imaging informatics in medicine
Deep learning-based denoising of low-dose medical CT images has received great attention both from academic researchers and physicians in recent years, and has shown important application value in clinical practice. In this work, a novel two-branch a...

Boosting cross-modal retrieval in remote sensing via a novel unified attention network.

Neural networks : the official journal of the International Neural Network Society
With the rapid advent and abundance of remote sensing data in different modalities, cross-modal retrieval tasks have gained importance in the research community. Cross-modal retrieval belongs to the research paradigm in which the query is of one moda...