AIMC Topic: Image Processing, Computer-Assisted

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A multi-context CNN ensemble for small lesion detection.

Artificial intelligence in medicine
In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial con...

Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods.

Artificial intelligence in medicine
Pressure injuries represent a tremendous healthcare challenge in many nations. Elderly and disabled people are the most affected by this fast growing disease. Hence, an accurate diagnosis of pressure injuries is paramount for efficient treatment. The...

Automated detection of focal cortical dysplasia using a deep convolutional neural network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Focal cortical dysplasia (FCD) is one of the commonest epileptogenic lesions, and is related to malformations of the cortical development. The findings on magnetic resonance (MR) images are important for the diagnosis and surgical planning of FCD. In...

Prediction of marbling score and carcass traits in Korean Hanwoo beef cattle using machine learning methods and synthetic minority oversampling technique.

Meat science
Pricing of Hanwoo beef in the Korean market is primarily based on meat quality, and particularly on marbling score. The ability to accurately predict marbling score early in the life of an animal is extremely valuable for producers to meet the requir...

Fast fit-free analysis of fluorescence lifetime imaging via deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI...

Real-Time Extraction of Important Surgical Phases in Cataract Surgery Videos.

Scientific reports
The present study aimed to conduct a real-time automatic analysis of two important surgical phases, which are continuous curvilinear capsulorrhexis (CCC), nuclear extraction, and three other surgical phases of cataract surgery using artificial intell...

Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Quantitative myocardial perfusion cardiac MRI can provide a fast and robust assessment of myocardial perfusion status for the noninvasive diagnosis of myocardial ischemia while being more objective than visual assessment. However, it curr...

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.

European radiology
OBJECTIVE: To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics feature...

New grading criterion for retinal haemorrhages in term newborns based on deep convolutional neural networks.

Clinical & experimental ophthalmology
BACKGROUND: To define a new quantitative grading criterion for retinal haemorrhages in term newborns based on the segmentation results of a deep convolutional neural network.

Artificial Intelligence in medical imaging practice: looking to the future.

Journal of medical radiation sciences
Artificial intelligence (AI) is heralded as the most disruptive technology to health services in the 21 century. Many commentary articles published in the general public and health domains recognise that medical imaging is at the forefront of these c...