AIMC Topic: Deep Learning
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SmartAlert: Machine learning-based patient-ventilator asynchrony detection system in intensive care units.
Computer methods and programs in biomedicine
Jun 21, 2025
BACKGROUND AND OBJECTIVE: Patient-ventilator asynchronies (PVA) are associated with ventilator-induced lung injury and increased mortality. Current detection methods rely on static thresholds, extensive preprocessing, or proprietary ventilator data. ...
A multimodal skin lesion classification through cross-attention fusion and collaborative edge computing.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 21, 2025
Skin cancer is a significant global health concern requiring early and accurate diagnosis to improve patient outcomes. While deep learning-based computer-aided diagnosis (CAD) systems have emerged as effective diagnostic support tools, they often fac...
Medical application of deep-learning-based head pose estimation from RGB image sequence.
Computers in biology and medicine
Jun 21, 2025
Recently, telemedicine has allowed doctor-to-patient or doctor-to-doctor consultations to tackle traditional problems: the COVID-19 pandemic, remote areas, long-time usage per visit, and dependence on family members in transportation. Nevertheless, f...
SE-ATT-YOLO- A deep learning driven ultrasound based respiratory motion compensation system for precision radiotherapy.
Computers in biology and medicine
Jun 21, 2025
OBJECTIVE: The therapeutic management of neoplasm employs high level energy beam to ablate malignant cells, which can cause collateral damage to adjacent normal tissue. Furthermore, respiration-induced organ motion, during radiotherapy can lead to si...
Deep learning dosiomics in grade 4 radiation-induced lymphopenia prediction in radiotherapy for esophageal cancer: a multi-center study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jun 21, 2025
PURPOSE: To investigate the feasibility and accuracy of using deep learning and dosiomics features, as well as their combination with dose-volume histogram (DVH) parameters and clinical factors to predict grade 4 radiation-induced lymphopenia (G4RIL)...
PMFF-Net: A deep learning-based image classification model for UIP, NSIP, and OP.
Computers in biology and medicine
Jun 20, 2025
BACKGROUND: High-resolution computed tomography (HRCT) is helpful for diagnosing interstitial lung diseases (ILD), but it largely depends on the experience of physicians. Herein, our study aims to develop a deep-learning-based classification model to...
BioTransX: A novel bi-former based hybrid model with bi-level routing attention for brain tumor classification with explainable insights.
Computers in biology and medicine
Jun 20, 2025
Brain tumors, known for their life-threatening implications, underscore the urgency of precise and interpretable early detection. Expertise remains essential for accurate identification through MRI scans due to the intricacies involved. However, the ...
Spindle Autoencoder-CNN hybrid model for cardiac arrhythmia classification.
Computers in biology and medicine
Jun 20, 2025
Cardiac arrhythmias, characterized by irregular heart function, disrupt normal blood circulation and are commonly detected using electrocardiograms (ECGs). ECG is widely preferred due to its cost-effectiveness, ease of application, and high reliabili...
Generative deep-learning-model based contrast enhancement for digital subtraction angiography using a text-conditioned image-to-image model.
Computers in biology and medicine
Jun 20, 2025
BACKGROUND: Digital subtraction angiography (DSA) is an essential imaging technique in interventional radiology, enabling detailed visualization of blood vessels by subtracting pre- and post-contrast images. However, reduced contrast, either accident...
Quantification of Breast Arterial Calcification in Mammograms Using a UNet-Based Deep Learning for Detecting Cardiovascular Disease.
Academic radiology
Jun 20, 2025
BACKGROUND: Breast arterial calcification (BAC) is increasingly recognized as a significant indicator of cardiovascular risk, necessitating improvements in detection and quantification methods through mammographic screening.
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