Identification of Colon Cancer Using Multi-Scale Feature Fusion Convolutional Neural Network Based on Shearlet Transform

Accurate segmentation of the blood pool of left ventricle (LV) and myocardium (or left ventricular epicardium, MYO) from cardiac magnetic resonance (MR) can help doctors to quantify LV ejection fraction and myocardial deformation. To reduce doctor’s burden of manual segmentation, in this study, we propose an automated and concurrent segmentation method of the LV and MYO. First, we employ a convolutional neural network (CNN) architecture to extract the region of interest (ROI) from short-axis cardiac cine MR images as a preprocessing step. Next, we present a multi-scale feature fusion (MSFF) CNN with a new weighted Dice index (WDI) loss function to get the concurrent segmentation of the LV and MYO. We use MSFF modules with three scales to extract different features, and then concatenate feature maps by the short and long skip connections in the encoder and decoder path to capture more complete context information and geometry structure for better segmentation. Finally, we compare the proposed method with Fully Convolutional Networks (FCN) and U-Net on the combined cardiac datasets from MICCAI 2009 and ACDC 2017. Experimental results demonstrate that the proposed method could perform effectively on LV and MYOs segmentation in the combined datasets, indicating its potential for clinical application.

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Deep Multi-scale Feature Fusion Convolutional Neural Network for Automatic Epilepsy Detection Using EEG Signals

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188949 ◽

2020 ◽

Author(s):

Hongshuai Qin ◽

Bin Deng ◽

Jiang Wang ◽

Guosheng Yi ◽

Ruofan Wang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Feature Fusion ◽

Eeg Signals ◽

Scale Feature ◽

Multi Scale

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Multi-Scale Feature-Guided Stereoscopic Video Quality Assessment Based on 3d Convolutional Neural Network

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9414231 ◽

2021 ◽

Author(s):

Yingjie Feng ◽

Sumei Li ◽

Yongli Chang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Quality Assessment ◽

Video Quality ◽

Video Quality Assessment ◽

Stereoscopic Video ◽

Scale Feature ◽

Multi Scale

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Multi-Scale Feature Fusion for Coal-Rock Recognition Based on Completed Local Binary Pattern and Convolution Neural Network

Entropy ◽

10.3390/e21060622 ◽

2019 ◽

Vol 21 (6) ◽

pp. 622 ◽

Cited By ~ 2

Author(s):

Xiaoyang Liu ◽

Wei Jing ◽

Mingxuan Zhou ◽

Yuxing Li

Keyword(s):

Neural Network ◽

Local Binary Pattern ◽

Feature Vector ◽

Feature Fusion ◽

Scale Feature ◽

Multi Scale ◽

Texture Information ◽

Deep Feature ◽

Coal Rock ◽

Rock Image

Automatic coal-rock recognition is one of the critical technologies for intelligent coal mining and processing. Most existing coal-rock recognition methods have some defects, such as unsatisfactory performance and low robustness. To solve these problems, and taking distinctive visual features of coal and rock into consideration, the multi-scale feature fusion coal-rock recognition (MFFCRR) model based on a multi-scale Completed Local Binary Pattern (CLBP) and a Convolution Neural Network (CNN) is proposed in this paper. Firstly, the multi-scale CLBP features are extracted from coal-rock image samples in the Texture Feature Extraction (TFE) sub-model, which represents texture information of the coal-rock image. Secondly, the high-level deep features are extracted from coal-rock image samples in the Deep Feature Extraction (DFE) sub-model, which represents macroscopic information of the coal-rock image. The texture information and macroscopic information are acquired based on information theory. Thirdly, the multi-scale feature vector is generated by fusing the multi-scale CLBP feature vector and deep feature vector. Finally, multi-scale feature vectors are input to the nearest neighbor classifier with the chi-square distance to realize coal-rock recognition. Experimental results show the coal-rock image recognition accuracy of the proposed MFFCRR model reaches 97.9167%, which increased by 2%–3% compared with state-of-the-art coal-rock recognition methods.

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