AIMC Topic: Image Processing, Computer-Assisted

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A deep learning method for counting white blood cells in bone marrow images.

BMC bioinformatics
BACKGROUND: Differentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and leukemia or analysis of a process of treatment. However, manually locating, identifying, and counti...

Leveraging deep neural networks to improve numerical and perceptual image quality in low-dose preclinical PET imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The amount of radiotracer injected into laboratory animals is still the most daunting challenge facing translational PET studies. Since low-dose imaging is characterized by a higher level of noise, the quality of the reconstructed images leaves much ...

MR image reconstruction using densely connected residual convolutional networks.

Computers in biology and medicine
MR image reconstruction techniques based on deep learning have shown their capacity for reducing MRI acquisition time and performance improvement compared to analytical methods. Despite the many challenges in training these rather large networks, nov...

Evaluation and Image Analysis of Enterprise Human Resource Management Based on the Simulated Annealing-Optimized BP Neural Network.

Computational intelligence and neuroscience
With the continuous development of social economy and the intensification of social competition, human resource management plays a more and more important role in the whole resource system. How to give full play to the advantages of human resources h...

Automated interpretation and analysis of bronchoalveolar lavage fluid.

International journal of medical informatics
BACKGROUND: The cytological analysis of bronchoalveolar lavage fluid (BALF) plays an essential role in the differential diagnosis of respiratory diseases. In recent years, deep learning has demonstrated excellent performance in image processing and o...

Deep learning is widely applicable to phenotyping embryonic development and disease.

Development (Cambridge, England)
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can auto...

DeepTOFSino: A deep learning model for synthesizing full-dose time-of-flight bin sinograms from their corresponding low-dose sinograms.

NeuroImage
PURPOSE: Reducing the injected activity and/or the scanning time is a desirable goal to minimize radiation exposure and maximize patients' comfort. To achieve this goal, we developed a deep neural network (DNN) model for synthesizing full-dose (FD) t...

Autonomous Thermal Vision Robotic System for Victims Recognition in Search and Rescue Missions.

Sensors (Basel, Switzerland)
Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to the application and development of new and improved neural networks to vision models together with m...

A novel Joint-Net model for recognizing small-bowel polyp images.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
INTRODUCTION: To automatically recognize polyps of enteroscopy images and avoid pathological change, a novel Joint-Net has been proposed.