AIMC Topic: Neural Networks, Computer

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An Incremental Class-Learning Approach with Acoustic Novelty Detection for Acoustic Event Recognition.

Sensors (Basel, Switzerland)
Acoustic scene analysis (ASA) relies on the dynamic sensing and understanding of stationary and non-stationary sounds from various events, background noises and human actions with objects. However, the spatio-temporal nature of the sound signals may ...

Bayesian supervised machine learning classification of neural networks with pathological perturbations.

Biomedical physics & engineering express
Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the...

Excitable networks for finite state computation with continuous time recurrent neural networks.

Biological cybernetics
Continuous time recurrent neural networks (CTRNN) are systems of coupled ordinary differential equations that are simple enough to be insightful for describing learning and computation, from both biological and machine learning viewpoints. We describ...

WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models.

IEEE journal of biomedical and health informatics
Biophysical protein-protein interactions perform dominant roles in the initiation and progression of many cancer-related pathways. A protein-protein interaction might play different roles in diverse cancer types. Hence, prioritizing the PPIs in each ...

The Convolutional Neural Networks Training With Channel-Selectivity for Human Activity Recognition Based on Sensors.

IEEE journal of biomedical and health informatics
Recently, the state-of-the-art performance in various sensor based human activity recognition (HAR) tasks have been acquired by deep learning, which can extract automatically features from raw data. In order to obtain the best accuracy, many static l...

Learn Fine-Grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images.

IEEE journal of biomedical and health informatics
Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the appearance of the captured anat...

Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation.

IEEE journal of biomedical and health informatics
Accurate segmentation of the polyps from colonoscopy images provides useful information for the diagnosis and treatment of colorectal cancer. Despite deep learning methods advance automatic polyp segmentation, their performance often degrades when ap...

ML-Net: Multi-Channel Lightweight Network for Detecting Myocardial Infarction.

IEEE journal of biomedical and health informatics
Due to the complexity of myocardial infarction (MI) waveform, most traditional automatic diagnosis models rarely detect it, while those able to detect MI often require high computing and storage capacity, rendering them unsuitable for portable device...

Synaptic Weight Evolution and Charge Trapping Mechanisms in a Synaptic Pass-Transistor Operation With a Direct Potential Output.

IEEE transactions on neural networks and learning systems
We present an intensive study on the weight modulation and charge trapping mechanisms of the synaptic transistor based on a pass-transistor concept for the direct voltage output. In this article, the pass-transistor concept for a metal-oxide-semicond...

A Deep Learning Approach for Colonoscopy Pathology WSI Analysis: Accurate Segmentation and Classification.

IEEE journal of biomedical and health informatics
Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology examination can identify cells of early-stage colon tumors in small tissue image slices. But, such examination is time-consuming and exhausting on high re...