AIMC Topic: Neural Networks, Computer

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Predicting nodal metastases in papillary thyroid carcinoma using artificial intelligence.

American journal of surgery
BACKGROUND: The presence of nodal metastases is important in the treatment of papillary thyroid carcinoma (PTC). We present our experience using a convolutional neural network (CNN) to predict the presence of nodal metastases in a series of PTC patie...

Self-organising maps for the exploration and classification of thin-layer chromatograms.

Talanta
Thin-layer chromatography (TLC) allows the swift analysis of larger sample sets in almost any laboratory. The obtained chromatograms are patterns of coloured zones that are conveniently evaluated and classified by visual inspection. This manual appro...

Hybrid Models for the simulation and prediction of chromatographic processes for protein capture.

Journal of chromatography. A
The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsol...

A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis.

Medical image analysis
Multiple Myeloma (MM) is a malignancy of plasma cells. Similar to other forms of cancer, it demands prompt diagnosis for reducing the risk of mortality. The conventional diagnostic tools are resource-intense and hence, these solutions are not easily ...

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive di...

Prediction of premature all-cause mortality in patients receiving peritoneal dialysis using modified artificial neural networks.

Aging
Premature all-cause mortality is high in patients receiving peritoneal dialysis (PD). The accurate and early prediction of mortality is critical and difficult. Three prediction models, the logistic regression (LR) model, artificial neural network (AN...

Learning deep features for dead and living breast cancer cell classification without staining.

Scientific reports
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer ce...

A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis.

Scientific reports
Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in ...

Deep learning applied to electroencephalogram data in mental disorders: A systematic review.

Biological psychology
In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictiv...