AIMC Topic: Neural Networks, Computer

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A Convolutional Neural Network and Graph Convolutional Network Based Framework for Classification of Breast Histopathological Images.

IEEE journal of biomedical and health informatics
The spatial correlation among different tissue components is an essential characteristic for diagnosis of breast cancers based on histopathological images. Graph convolutional network (GCN) can effectively capture this spatial feature representation,...

A Drug Recommendation Model Based on Message Propagation and DDI Gating Mechanism.

IEEE journal of biomedical and health informatics
Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommen...

SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images.

IEEE journal of biomedical and health informatics
Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due ...

Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion.

IEEE journal of biomedical and health informatics
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combi...

Unsupervised Gait Phase Estimation With Domain-Adversarial Neural Network and Adaptive Window.

IEEE journal of biomedical and health informatics
The performanceof previous machine learning models for gait phase is only satisfactory under limited conditions. First, they produce accurate estimations only when the ground truth of the gait phase (of the target subject) is known. In contrast, when...

A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data.

Computational intelligence and neuroscience
Intelligent fault diagnosis methods based on deep learning have achieved much progress in recent years. However, there are two major factors causing serious degradation of the performance of these algorithms in real industrial applications, i.e., lim...

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller.

Computational intelligence and neuroscience
The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy...

DC Motor Control Technology Based on Multisensor Information Fusion.

Computational intelligence and neuroscience
To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one...

Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach.

Computational and mathematical methods in medicine
Brain cancer is a rare and deadly disease with a slim chance of survival. One of the most important tasks for neurologists and radiologists is to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can ...

Deep learning based lithology classification of drill core images.

PloS one
Drill core lithology is an important indicator reflecting the geological conditions of the drilling area. Traditional lithology identification usually relies on manual visual inspection, which is time-consuming and professionally demanding. In recent...