AIMC Topic: Image Processing, Computer-Assisted

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BIRNet: Brain image registration using dual-supervised fully convolutional networks.

Medical image analysis
In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that i...

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders.

Medical physics
PURPOSE: The purpose of this study is to introduce and evaluate the mixed structure regularization (MSR) approach for a deep sparse autoencoder aimed at unsupervised abnormality detection in medical images. Unsupervised abnormality detection based on...

A Novel Technique for Multi Biometric Cryptosystem Using Fuzzy Vault.

Journal of medical systems
Biometric authentication is the process of recognizing a person by means of his\her psychological or behavioral traits. One of the most important issues faced by the biometric system developer is to protect the template obtained from the biometric of...

Complexity perception classification method for tongue constitution recognition.

Artificial intelligence in medicine
The body constitution is much related to the diseases and the corresponding treatment programs in Traditional Chinese Medicine. It can be recognized by the tongue image diagnosis, so that it is essentially regarded as a problem of tongue image classi...

CNNs for automatic glaucoma assessment using fundus images: an extensive validation.

Biomedical engineering online
BACKGROUND: Most current algorithms for automatic glaucoma assessment using fundus images rely on handcrafted features based on segmentation, which are affected by the performance of the chosen segmentation method and the extracted features. Among ot...

Unsupervised domain adaptation for medical imaging segmentation with self-ensembling.

NeuroImage
Recent advances in deep learning methods have redefined the state-of-the-art for many medical imaging applications, surpassing previous approaches and sometimes even competing with human judgment in several tasks. Those models, however, when trained ...

Governance of automated image analysis and artificial intelligence analytics in healthcare.

Clinical radiology
The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiologists) will become redundant. It is still moot as to whether AI will replace radiologists in day-to-day clinical practice, but more AI applications are...

Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN.

IEEE journal of biomedical and health informatics
Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and...

Data-driven synthetic MRI FLAIR artifact correction via deep neural network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow,...

A novel end-to-end brain tumor segmentation method using improved fully convolutional networks.

Computers in biology and medicine
Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active research topic in medical image analysis since it provides doctors with meaningful and reliable quantitative information in diagnosing and monitoring neurolo...