AIMC Topic: Neural Networks, Computer

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Structure-response relationship in electrospray ionization-mass spectrometry of sartans by artificial neural networks.

Journal of chromatography. A
Quantitative structure-property relationship (QSPR) methods are based on the hypothesis that changes in the molecular structure are reflected in changes in the observed property of the molecule. Artificial neural network is a technique of data analys...

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study.

PloS one
BACKGROUND: Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA ...

HEp-2 Cell Image Classification With Deep Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which...

Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images.

IEEE transactions on medical imaging
Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is...

Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines.

IEEE transactions on medical imaging
The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning m...

Low-dimensional dynamics of structured random networks.

Physical review. E
Using a generalized random recurrent neural network model, and by extending our recently developed mean-field approach [J. Aljadeff, M. Stern, and T. Sharpee, Phys. Rev. Lett. 114, 088101 (2015)], we study the relationship between the network connect...

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images.

IEEE transactions on medical imaging
Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce en...

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars.

Scientific reports
Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and ...

Image and geometry processing with Oriented and Scalable Map.

Neural networks : the official journal of the International Neural Network Society
We turn the Self-organizing Map (SOM) into an Oriented and Scalable Map (OS-Map) by generalizing the neighborhood function and the winner selection. The homogeneous Gaussian neighborhood function is replaced with the matrix exponential. Thus we can s...

Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition.

IEEE transactions on medical imaging
In general image recognition problems, discriminative information often lies in local image patches. For example, most human identity information exists in the image patches containing human faces. The same situation stays in medical images as well. ...