AIMC Topic: Image Processing, Computer-Assisted

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Determining Exception Context in Assembly Operations from Multimodal Data.

Sensors (Basel, Switzerland)
Robot assembly tasks can fail due to unpredictable errors and can only continue with the manual intervention of a human operator. Recently, we proposed an exception strategy learning framework based on statistical learning and context determination, ...

Technical note: A method to synthesize magnetic resonance images in different patient rotation angles with deep learning for gantry-free radiotherapy.

Medical physics
BACKGROUND: Recently, patient rotating devices for gantry-free radiotherapy, a new approach to implement external beam radiotherapy, have been introduced. When a patient is rotated in the horizontal position, gravity causes anatomic deformation. For ...

Deep, deep learning with BART.

Magnetic resonance in medicine
PURPOSE: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI.

Application of a convolutional neural network to the quality control of MRI defacing.

Computers in biology and medicine
Large-scale neuroimaging datasets present unique challenges for automated processing pipelines. Motivated by a large clinical trials dataset with over 235,000 MRI scans, we consider the challenge of defacing - anonymisation to remove identifying faci...

Research progress in digital pathology: A bibliometric and visual analysis based on Web of Science.

Pathology, research and practice
BACKGROUND: The development of whole slide image and deep neural network technologies has contributed to the paradigm shift in diagnostic pathology and has received much attention from researchers, with related publications increasing yearly and "exp...

LANCE: a Label-Free Live Apoptotic and Necrotic Cell Explorer Using Convolutional Neural Network Image Analysis.

Analytical chemistry
Identifying and quantifying cell death is the basis for all cell death research. Current methods for obtaining these quantitative measurements rely on established biomarkers, yet the marker-based approach suffers from limited marker specificity, high...

Development and validation of a deep learning-based laparoscopic system for improving video quality.

International journal of computer assisted radiology and surgery
PURPOSE: A clear surgical field of view is a prerequisite for successful laparoscopic surgery. Surgical smoke, image blur, and lens fogging can affect the clarity of laparoscopic imaging. We aimed to develop a real-time assistance system (namely LVQI...

Image-based time series forecasting: A deep convolutional neural network approach.

Neural networks : the official journal of the International Neural Network Society
Inspired by the successful use of deep learning in computer vision, in this paper we introduce ForCNN, a novel deep learning method for univariate time series forecasting that mixes convolutional and dense layers in a single neural network. Instead o...

Improving Lateral Resolution in 3-D Imaging With Micro-beamforming Through Adaptive Beamforming by Deep Learning.

Ultrasound in medicine & biology
There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral reso...

Multi-Object Detection in Security Screening Scene Based on Convolutional Neural Network.

Sensors (Basel, Switzerland)
The technique for target detection based on a convolutional neural network has been widely implemented in the industry. However, the detection accuracy of X-ray images in security screening scenarios still requires improvement. This paper proposes a ...