AIMC Topic: Image Processing, Computer-Assisted

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Neural Network-Oriented Big Data Model for Yoga Movement Recognition.

Computational intelligence and neuroscience
The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for c...

FCL-Net: Towards accurate edge detection via Fine-scale Corrective Learning.

Neural networks : the official journal of the International Neural Network Society
Integrating multi-scale predictions has become a mainstream paradigm in edge detection. However, most existing methods mainly focus on effective feature extraction and multi-scale feature fusion while ignoring the low learning capacity in fine-level ...

Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma.

Scientific reports
Even a tiny functioning pituitary adenoma could cause symptoms; hence, accurate diagnosis and treatment are crucial for management. However, it is difficult to diagnose a small pituitary adenoma using conventional MR sequence. Deep learning-based rec...

GuidedStyle: Attribute knowledge guided style manipulation for semantic face editing.

Neural networks : the official journal of the International Neural Network Society
Although significant progress has been made in synthesizing high-quality and visually realistic face images by unconditional Generative Adversarial Networks (GANs), there is still a lack of control over the generation process in order to achieve sema...

Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm.

Scientific data
Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a...

Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases.

Journal of healthcare engineering
This study focused on the application of deep learning algorithms in the segmentation of CT images, so as to diagnose chronic kidney diseases accurately and quantitatively. First, the residual dual-attention module (RDA module) was used for automatic...

Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images.

Magnetic resonance imaging
Automated brain tumour segmentation from post-operative images is a clinically relevant yet challenging problem. In this study, an automated method for segmenting brain tumour into its subregions has been developed. The dataset consists of multimodal...

Deep Learning Image Analysis of High-Throughput Toxicology Assay Images.

SLAS discovery : advancing life sciences R & D
High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-con...

Explainable deep learning ensemble for food image analysis on edge devices.

Computers in biology and medicine
Food recognition systems recently garnered much research attention in the relevant field due to their ability to obtain objective measurements for dietary intake. This feature contributes to the management of various chronic conditions. Challenges su...

A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.

Cell reports methods
MOTIVATION: Quantitative studies of cellular morphodynamics rely on extracting leading-edge velocity time series based on accurate cell segmentation from live cell imaging. However, live cell imaging has numerous challenging issues regarding accurate...