Feature Extraction Efficient for Face Verification Based on Residual Network Architecture

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

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Bearing Fault Identification Based on Deep Convolution Residual Network

Mechanika ◽

10.5755/j02.mech.28265 ◽

2021 ◽

Vol 27 (3) ◽

pp. 229-236

Author(s):

Tong ZHOU ◽

Yuan LI ◽

Yijia JING ◽

Yifei TONG

Keyword(s):

Feature Extraction ◽

Deep Neural Networks ◽

High Accuracy ◽

Normal Operation ◽

Fault Identification ◽

Mechanical Equipment ◽

Residual Network ◽

Bearing Fault ◽

Fitting Ability ◽

Very High

Bearings are important parts in industrial production and are related to the normal operation of mechanical equipment. For bearing fault identification， traditional method often includes feature extraction, which involves professional prior knowledge and is time-consuming. This paper proposes the deep convolution residual network (DCRN) to identify the bearing fault. Based on the end-to-end learning characteristics of deep neural networks, this method can directly use raw data for training, and does not require feature extraction. Moreover, under the effect of skip connection, DCRN can exert the powerful fitting ability of deep neural network. In this paper, by stacking residual blocks, three different architecture of DCRN are designed and all three achieve very high accuracy, respectively 99.60%, 99.71% and 99.81%. Compared with other methods, DCRN have better generalization performance.

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Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks

Frontiers in Oncology ◽

10.3389/fonc.2021.665929 ◽

2021 ◽

Vol 11 ◽

Author(s):

Hao Fu ◽

Weiming Mi ◽

Boju Pan ◽

Yucheng Guo ◽

Junjie Li ◽

...

Keyword(s):

Feature Extraction ◽

Pancreatic Ductal Adenocarcinoma ◽

Network Architecture ◽

Extraction Methods ◽

Ductal Adenocarcinoma ◽

Deep Convolutional Neural Networks ◽

Histopathological Images ◽

Feature Based ◽

Detection And Diagnosis ◽

Histopathology Image Analysis

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.

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Deep Convolutional Neural Networks with Merge-and-Run Mappings

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/440 ◽

2018 ◽

Cited By ~ 8

Author(s):

Liming Zhao ◽

Mingjie Li ◽

Depu Meng ◽

Xi Li ◽

Zhaoxiang Zhang ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Information Flow ◽

Building Block ◽

Network Architecture ◽

Time Complexity ◽

Residual Network ◽

Deep Convolutional Neural Networks ◽

Testing Error ◽

Simple Network

A deep residual network, built by stacking a sequence of residual blocks, is easy to train, because identity mappings skip residual branches and thus improve information flow. To further reduce the training difficulty, we present a simple network architecture, deep merge-and-run neural networks. The novelty lies in a modularized building block, merge-and-run block, which assembles residual branches in parallel through a merge-and-run mapping: average the inputs of these residual branches (Merge), and add the average to the output of each residual branch as the input of the subsequent residual branch (Run), respectively. We show that the merge-and-run mapping is a linear idempotent function in which the transformation matrix is idempotent, and thus improves information flow, making training easy. In comparison with residual networks, our networks enjoy compelling advantages: they contain much shorter paths and the width, i.e., the number of channels, is increased, and the time complexity remains unchanged. We evaluate the performance on the standard recognition tasks. Our approach demonstrates consistent improvements over ResNets with the comparable setup, and achieves competitive results (e.g., 3.06% testing error on CIFAR-10, 17.55% on CIFAR-100, 1.51% on SVHN).

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