AIMC Topic: Deep Learning
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A machine learning toolkit assisted approach for IMRT fluence map optimization: feasibility and advantages.
Biomedical physics & engineering express
Apr 22, 2025
. Traditional machine learning (ML) and deep learning (DL) applications in treatment planning rely on complex model architectures and large, high-quality training datasets. However, they cannot fully replace the conventional optimization process. Thi...
Advancing Single-Plane Wave Ultrasound Imaging With Implicit Multiangle Acoustic Synthesis via Deep Learning.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Apr 22, 2025
Plane wave imaging (PWI) is pivotal in medical ultrasound (US), prized for its ultrafast capabilities essential for real-time physiological monitoring. Traditionally, enhancing image quality in PWI has necessitated an increase in the number of plane ...
Tissue Clutter Filtering Methods in Ultrasound Localization Microscopy Based on Complex-Valued Networks and Knowledge Distillation.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Apr 22, 2025
Ultrasound localization microscopy (ULM) is a blood flow imaging technique that utilizes micrometer-sized microbubbles (MBs) as contrast agents to achieve high-resolution microvessel reconstruction through precise localization and tracking of MBs. Th...
Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Apr 22, 2025
Resolving arterial flows is essential for understanding cardiovascular pathologies, improving diagnosis, and monitoring patient condition. Ultrasound contrast imaging uses microbubbles to enhance the scattering of the blood pool, allowing for real-ti...
A deep learning-based ensemble for autism spectrum disorder diagnosis using facial images.
PloS one
Apr 22, 2025
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder leading to an inability to socially communicate and in extreme cases individuals are completely dependent on caregivers. ASD detection at early ages is crucial as early detection can red...
scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types.
IET systems biology
Apr 22, 2025
Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often fa...
A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 22, 2025
Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to the overlap of initial symptoms with normal aging processes. Given that no cure exists and current ...
Causal recurrent intervention for cross-modal cardiac image segmentation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 21, 2025
Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise information about cardiac structure or function for potential signs by leveraging specific imaging modalities...
OrgaMeas: A pipeline that integrates all the processes of organelle image analysis.
Biochimica et biophysica acta. Molecular cell research
Apr 21, 2025
Although image analysis has emerged as a key technology in the study of organelle dynamics, the commonly used image-processing methods, such as threshold-based segmentation and manual setting of regions of interests (ROIs), are error-prone and labori...
Early operative difficulty assessment in laparoscopic cholecystectomy via snapshot-centric video analysis.
International journal of computer assisted radiology and surgery
Apr 21, 2025
PURPOSE: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limited efforts explore LCOD using intraoperative video data. Early recogni...
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