AIMC Topic: Image Processing, Computer-Assisted

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Can Synthetic Images Improve CNN Performance in Wound Image Classification?

Studies in health technology and informatics
For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large ...

Deep learning method of stochastic reconstruction of three-dimensional digital cores from a two-dimensional image.

Physical review. E
Digital cores can characterize the true internal structure of rocks at the pore scale. This method has become one of the most effective ways to quantitatively analyze the pore structure and other properties of digital cores in rock physics and petrol...

[CT and MRI fusion based on generative adversarial network and convolutional neural networks under image enhancement].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fus...

CT medical image segmentation algorithm based on deep learning technology.

Mathematical biosciences and engineering : MBE
For the problems of blurred edges, uneven background distribution, and many noise interferences in medical image segmentation, we proposed a medical image segmentation algorithm based on deep neural network technology, which adopts a similar U-Net ba...

A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution.

Bioinformatics (Oxford, England)
MOTIVATION: Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundament...

Effective and efficient active learning for deep learning-based tissue image analysis.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning attained excellent results in digital pathology recently. A challenge with its use is that high quality, representative training datasets are required to build robust models. Data annotation in the domain is labor intensive ...

Automatic detection of adenoid hypertrophy on cone-beam computed tomography based on deep learning.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: This study proposed an automatic diagnosis method based on deep learning for adenoid hypertrophy detection on cone-beam computed tomography.

RESEARCH PROGRESS OF DEEP LEARNING IN LOW-DOSE CT IMAGE DENOISING.

Radiation protection dosimetry
Low-dose computed tomography (CT) will increase noise and artefacts while reducing the radiation dose, which will adversely affect the diagnosis of radiologists. Low-dose CT image denoising is a challenging task. There are essential differences betwe...

Facial expression recognition using lightweight deep learning modeling.

Mathematical biosciences and engineering : MBE
Facial expression is a type of communication and is useful in many areas of computer vision, including intelligent visual surveillance, human-robot interaction and human behavior analysis. A deep learning approach is presented to classify happy, sad,...