AIMC Topic: Deep Learning

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Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface.

IEEE journal of biomedical and health informatics
Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurological disorders. The correlation between these somatosensory stimulations and dynamical brain response...

Advancing Acoustic Droplet Vaporization for Tissue Characterization Using Quantitative Ultrasound and Transfer Learning.

IEEE transactions on bio-medical engineering
Acoustic droplet vaporization (ADV) is an emerging technique with expanding applications in biomedical ultrasound. ADV-generated bubbles can function as microscale probes that provide insights into the mechanical properties of their surrounding micro...

Hierarchical Multi-Class Group Correlation Learning Network for Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Hierarchical approaches have been tremendously successful at multi-label segmentation. However, it has been shown they may seriously suffer from the problem of only imposing constraints on shallow layers while ignoring deep relationships in the label...

P2TC: A Lightweight Pyramid Pooling Transformer-CNN Network for Accurate 3D Whole Heart Segmentation.

IEEE journal of biomedical and health informatics
Cardiovascular disease is a leading global cause of death, requiring accurate heart segmentation for diagnosis and surgical planning. Deep learning methods have been demonstrated to achieve superior performances in cardiac structures segmentation. Ho...

Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking.

IEEE transactions on bio-medical engineering
The assessment of surgical skill is crucial for indicating a surgeon's proficiency. While motion analysis of surgical tools is widely used in endoscopic surgery, it is not commonly applied to open surgery. Instead, open surgery skill assessment relie...

Learning Sensor Sample-Reweighting for Dynamic Early-Exit Activity Recognition Via Meta Learning.

IEEE journal of biomedical and health informatics
During recent years, dynamic early-exit has provided a promising paradigm to improve the computational efficiency of deep neural networks by constructing multiple classifiers to let easy samples exit at shallow layers while avoiding redundant computa...

A Forward and Backward Compatible Framework for Few-Shot Class-Incremental Pill Recognition.

IEEE transactions on neural networks and learning systems
Automatic pill recognition (APR) systems are crucial for enhancing hospital efficiency, assisting visually impaired individuals, and preventing cross-infection. However, most existing deep learning-based pill recognition systems can only perform clas...

Meteorological and traffic effects on air pollutants using Bayesian networks and deep learning.

Journal of environmental sciences (China)
Traffic emissions have become the major air pollution source in urban areas. Therefore, understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models. Using re...

Deep learning-enhanced zero echo time MRI for glenohumeral assessment in shoulder instability: a comparative study with CT.

Skeletal radiology
PURPOSE: To evaluate image quality and lesion conspicuity of zero echo time (ZTE) MRI reconstructed with deep learning (DL)-based algorithm versus conventional reconstruction and to assess DL ZTE performance against CT for bone loss measurements in s...