AIMC Topic: Image Processing, Computer-Assisted

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Image learning to accurately identify complex mixture components.

The Analyst
The study of complex mixtures is very important for exploring the evolution of natural phenomena, but the complexity of the mixtures greatly increases the difficulty of material information extraction. Image perception-based machine-learning techniqu...

Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between e...

Whole brain segmentation with full volume neural network.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions, usually ...

Robust whole slide image analysis for cervical cancer screening using deep learning.

Nature communications
Computer-assisted diagnosis is key for scaling up cervical cancer screening. However, current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to generalize for diverse staining and imaging, and show sub-optimal clinica...

Landscape Planning and Image Analysis Based on Multipopulation Coevolution Particle Swarm Radial Basis Function Neural Network Algorithm.

Computational intelligence and neuroscience
Urban landscape planning and design is not only closely related to people's living environment, but also has an important impact on urban planning and development. However, there are some problems in landscape planning and design, such as excellent c...

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

Scientific reports
Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of indiv...

A Method of CT Image Denoising Based on Residual Encoder-Decoder Network.

Journal of healthcare engineering
Low-dose computed tomography (CT) has proved effective in lowering radiation risk for the patients, but the resultant noise and bar artifacts in CT images can be a disturbance for medical diagnosis. The difficulty of modeling statistical features in ...

Progressively Inpainting Images Based on a Forked-Then-Fused Decoder Network.

Sensors (Basel, Switzerland)
Image inpainting aims to fill in corrupted regions with visually realistic and semantically plausible contents. In this paper, we propose a progressive image inpainting method, which is based on a forked-then-fused decoder network. A unit called PC-R...

Machine learning methods for automated classification of tumors with papillary thyroid carcinoma-like nuclei: A quantitative analysis.

PloS one
When approaching thyroid gland tumor classification, the differentiation between samples with and without "papillary thyroid carcinoma-like" nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing ...

Evaluation of Scopio Labs X100 Full Field PBS: The first high-resolution full field viewing of peripheral blood specimens combined with artificial intelligence-based morphological analysis.

International journal of laboratory hematology
BACKGROUND: Current digital cell imaging systems perform peripheral blood smear (PBS) analysis in limited regions of the PBS and require the support of manual microscopy without achieving full digital microscopy. We report a multicenter study that va...