AIMC Topic: Deep Learning
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Leveraging deep learning for improving parameter extraction from perfusion MR images: A narrative review.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Apr 11, 2025
BACKGROUND: Perfusion magnetic resonance imaging (MRI) is a non-invasive technique essential for assessing tissue microcirculation and perfusion dynamics. Various perfusion MRI techniques like Dynamic Contrast-Enhanced (DCE), Dynamic Susceptibility C...
Predicting the efficacy of microwave ablation of benign thyroid nodules from ultrasound images using deep convolutional neural networks.
BMC medical informatics and decision making
Apr 11, 2025
BACKGROUND: Thyroid nodules are frequent in clinical settings, and their diagnosis in adults is growing, with some persons experiencing symptoms. Ultrasound-guided thermal ablation can shrink nodules and alleviate discomfort. Because the degree and r...
Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images.
Scientific reports
Apr 11, 2025
Lymphedema is a chronic condition characterized by lymphatic fluid accumulation, primarily affecting the limbs. Its diagnosis is challenging due to symptom overlap with conditions like chronic venous insufficiency (CVI), deep vein thrombosis (DVT), a...
Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.
Scientific reports
Apr 11, 2025
Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims t...
Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification.
Scientific reports
Apr 11, 2025
To address the public health issue of renal failure and the global shortage of nephrologists, an AI-based system has been developed to automatically identify kidney diseases. Recent advancements in machine learning, deep learning (DL), and artificial...
Detecting arousals and sleep from respiratory inductance plethysmography.
Sleep & breathing = Schlaf & Atmung
Apr 11, 2025
PURPOSE: Accurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polysomnography (PSG) is the gold standard for such assessments but is costly and requires overnight monit...
Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.
Biomedical physics & engineering express
Apr 11, 2025
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...
CWMS-GAN: A small-sample bearing fault diagnosis method based on continuous wavelet transform and multi-size kernel attention mechanism.
PloS one
Apr 11, 2025
In industrial production, obtaining sufficient bearing fault signals is often extremely difficult, leading to a significant degradation in the performance of traditional deep learning-based fault diagnosis models. Many recent studies have shown that ...
A deep learning-based approach for the detection of cucumber diseases.
PloS one
Apr 11, 2025
Cucumbers play a significant role as a greenhouse crop globally. In numerous countries, they are fundamental to dietary practices, contributing significantly to the nutritional patterns of various populations. Due to unfavorable environmental conditi...
μGlia-Flow, an automatic workflow for microglia segmentation and classification.
Journal of neuroscience methods
Apr 10, 2025
BACKGROUND: Microglia are important immune cells in the central nervous system, playing a key role in various pathological processes. The morphological diversity of microglia is closely linked to the development of brain diseases, yet accurate segmen...
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