AIMC Topic: Deep Learning

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Multimodal prediction of metastatic relapse using federated deep learning in soft-tissue sarcoma with a complex genomic profile.

Scientific reports
Soft Tissue sarcomas (STS) are a group of heterogeneous and complex diseases where being able to predict the appearance of metastases is key to inform clinical decisions, especially the prescription of adjuvant chemotherapy. We developed SarcNet: a m...

Integrating deep learning and radiomics for preoperative glioma grading using multi-center MRI data.

Scientific reports
Accurate preoperative glioma grading remains a critical challenge in neuro-oncology. This study presents a novel integrated approach combining deep learning architectures with radiomics features derived from multi-parametric MRI to improve preoperati...

Exploring the therapeutic effects of continuous kidney replacement therapy in patients with severe acidosis using deep learning-based causal inference.

Scientific reports
Continuous kidney replacement therapy (CKRT) is an essential treatment for uncontrolled severe metabolic acidosis. However, CKRT can increase workload and lead to complications, thus necessitating its selective application to patients who stand to be...

SATU-net: a shadow adaptive tracing U-net for gastric cavity segmentation based on the principle of ultrasound imaging.

Scientific reports
Accurate segmentation of gastric cavities from ultrasound images remains a challenging task due to the presence of ultrasound shadow and varying anatomical structures. To address these challenges, we collected a Gastric Ultrasound Image (GUSI) datase...

Multi-strategy dung beetle optimization for robust indoor object detection and tracking for visually impaired people with hybrid deep learning networks.

Scientific reports
Visually impaired people generally face many troubles in their everyday lives, and technical involvement might help them perform these tasks. Object detection is a significant aspect of computer vision (CV) and machine learning (ML), which plays a su...

Implementing ensemble of deep learning model with optimization techniques for human activity recognition to assist individuals with disabilities.

Scientific reports
Recent human activity recognition (HAR) developments have allowed numerous applications like healthcare, smart homes, and improved manufacturing. Activity recognition plays a crucial part in improving human well-being by capturing behavioral data, en...

DANet a lightweight dilated attention network for malaria parasite detection.

Scientific reports
Malaria remains a critical global health challenge, requiring accurate and efficient diagnostic tools, particularly in developing countries with limited medical expertise. Detecting malaria parasites from red blood cell (RBC) blood smear images is ch...

A lightweight network for brain MRI segmentation.

Scientific reports
Brain MRI segmentation plays a crucial role in medical imaging, aiding in the identification and monitoring of brain diseases. This research presents a novel deep learning-based framework designed to achieve high segmentation accuracy while maintaini...

3D deep learning-based muscle volume quantification from thoracic CT as a surrogate for DXA-Derived appendicular muscle mass in older adults.

Aging clinical and experimental research
BACKGROUND: In order to identify patients with sarcopenia, the use of routine imaging could provide valuable support. One of the most common radiological examinations, especially in geriatric inpatient care, is CT thoracic imaging. Therefore, it woul...

Detection of climate change signals using precipitation and temperature time series by a hybrid deep learning framework.

Environmental monitoring and assessment
Climate change is one of the most extreme challenges of the twenty-first century. Precipitation (pr) and temperature variability are key indicators of climate change detection. Whereas hybrid deep learning (DL) models have been widely applied, their ...