scholarly journals DW-Net: Dynamic Multi-Hierarchical Weighting Segmentation Network for Joint Segmentation of Retina Layers With Choroid Neovascularization

2021 ◽  
Vol 15 ◽  
Author(s):  
Lianyu Wang ◽  
Meng Wang ◽  
Tingting Wang ◽  
Qingquan Meng ◽  
Yi Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Quantitative Measurement ◽  
State Of The Art ◽  
Complex Structure ◽  
Subsequent Treatment ◽  
Experimental Results ◽  
Informative Feature ◽  
Retinal Layers ◽  
Selection Of

Choroid neovascularization (CNV) is one of the blinding factors. The early detection and quantitative measurement of CNV are crucial for the establishment of subsequent treatment. Recently, many deep learning-based methods have been proposed for CNV segmentation. However, CNV is difficult to be segmented due to the complex structure of the surrounding retina. In this paper, we propose a novel dynamic multi-hierarchical weighting segmentation network (DW-Net) for the simultaneous segmentation of retinal layers and CNV. Specifically, the proposed network is composed of a residual aggregation encoder path for the selection of informative feature, a multi-hierarchical weighting connection for the fusion of detailed information and abstract information, and a dynamic decoder path. Comprehensive experimental results show that our proposed DW-Net achieves better performance than other state-of-the-art methods.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

scholarly journals Deep Learning Approach for Automatic Microaneurysms Detection

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 542
Author(s):  
Muhammad Mateen ◽  
Tauqeer Safdar Malik ◽  
Shaukat Hayat ◽  
Musab Hameed ◽  
Song Sun ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
Classification Accuracy ◽  
Vision Loss ◽  
State Of The Art ◽  
The State ◽  
Learning Approach ◽  
Manual Analysis

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely “E-Ophtha” and “DIARETDB1”, and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection.

scholarly journals A Feasibility Test on Preventing PRMDs Based on Deep Learning

2019 ◽  
Vol 33 ◽  
pp. 10005-10006
Author(s):  
So-Hyun Park ◽  
Sun-Young Ihm ◽  
Aziz Nasridinov ◽  
Young-Ho Park
Keyword(s):  
Deep Learning ◽  
Musculoskeletal Disorders ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Video Classification ◽  
Learning Paradigm ◽  
Feasibility Test ◽  
Piano Playing

This study proposes a method to reduce the playing-related musculoskeletal disorders (PRMDs) that often occur among pianists. Specifically, we propose a feasibility test that evaluates several state-of-the-art deep learning algorithms to prevent injuries of pianist. For this, we propose (1) a C3P dataset including various piano playing postures and show (2) the application of four learning algorithms, which demonstrated their superiority in video classification, to the proposed C3P datasets. To our knowledge, this is the first study that attempted to apply the deep learning paradigm to reduce the PRMDs in pianist. The experimental results demonstrated that the classification accuracy is 80% on average, indicating that the proposed hypothesis about the effectiveness of the deep learning algorithms to prevent injuries of pianist is true.

Sanitizing hidden activations for improving adversarial robustness of convolutional neural networks

2021 ◽  
pp. 1-11
Author(s):  
Tianshi Mu ◽  
Kequan Lin ◽  
Huabing Zhang ◽  
Jian Wang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Black Box ◽  
Experimental Results ◽  
Amplification Effect ◽  
Wide Range ◽  
Adversarial Examples

Deep learning is gaining significant traction in a wide range of areas. Whereas, recent studies have demonstrated that deep learning exhibits the fatal weakness on adversarial examples. Due to the black-box nature and un-transparency problem of deep learning, it is difficult to explain the reason for the existence of adversarial examples and also hard to defend against them. This study focuses on improving the adversarial robustness of convolutional neural networks. We first explore how adversarial examples behave inside the network through visualization. We find that adversarial examples produce perturbations in hidden activations, which forms an amplification effect to fool the network. Motivated by this observation, we propose an approach, termed as sanitizing hidden activations, to help the network correctly recognize adversarial examples by eliminating or reducing the perturbations in hidden activations. To demonstrate the effectiveness of our approach, we conduct experiments on three widely used datasets: MNIST, CIFAR-10 and ImageNet, and also compare with state-of-the-art defense techniques. The experimental results show that our sanitizing approach is more generalized to defend against different kinds of attacks and can effectively improve the adversarial robustness of convolutional neural networks.

scholarly journals Global and Local Attention-Based Free-Form Image Inpainting

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3204
Author(s):  
S. M. Nadim Uddin ◽  
Yong Ju Jung
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Image Inpainting ◽  
Experimental Results ◽  
Free Form ◽  
Local Similarity ◽  
Global And Local ◽  
Similarity Information

Deep-learning-based image inpainting methods have shown significant promise in both rectangular and irregular holes. However, the inpainting of irregular holes presents numerous challenges owing to uncertainties in their shapes and locations. When depending solely on convolutional neural network (CNN) or adversarial supervision, plausible inpainting results cannot be guaranteed because irregular holes need attention-based guidance for retrieving information for content generation. In this paper, we propose two new attention mechanisms, namely a mask pruning-based global attention module and a global and local attention module to obtain global dependency information and the local similarity information among the features for refined results. The proposed method is evaluated using state-of-the-art methods, and the experimental results show that our method outperforms the existing methods in both quantitative and qualitative measures.

scholarly journals An interpretable framework for investigating the neighborhood effect in POI recommendation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255685
Author(s):  
Guangchao Yuan ◽  
Munindar P. Singh ◽  
Pradeep K. Murukannaiah
Keyword(s):  
Deep Learning ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Experimental Results ◽  
Neighborhood Effect ◽  
Data Set ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Geographical Characteristics

Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect, which captures a user’s POI visiting behavior based on the user’s preference not only to a POI, but also to the POI’s neighborhood. To provide an interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp data set to evaluate how different aspects of the neighborhood effect affect a user’s POI visiting behavior. Second, we propose a deep learning–based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization–based POI recommendation techniques.

scholarly journals Application of deep learning and computer vision frameworks for solving video context prediction problem

2016 ◽  
pp. 164-169
Author(s):  
D. Voloshyn ◽  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Prediction Problem ◽  
Detection Problem ◽  
Application Architecture ◽  
Context Detection

Authors describe an application for solving video context detection problem. Application architecture use state-of-the-art deap learning TensorFlow framework together with the computer vision library OpenCV in isolated agent environment. The experimental results are shown to demonstrate the effectiveness of developed product.

Variable Scale Compressive Tracking Based on Structural Constraint Sample

2014 ◽  
Vol 488-489 ◽  
pp. 1074-1078
Author(s):  
Lu Ping Zhang ◽  
Meng Cai ◽  
Biao Li ◽  
Lu Ping Wang
Keyword(s):  
State Of The Art ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Structural Constraint ◽  
Scale Problem ◽  
Scale Selection ◽  
Compressive Tracking ◽  
Image Patches ◽  
Basic Attitude ◽  
Selection Of

A variable scale compressive tracking algorithm based on structural constraint sample is presented to solve the variable scale problem in this paper. A number of scanning windows with different scales and positions are obtained by structural constraint sampling.Some sparse random sensing matrices with different scales that can be computed offline easily are adopted to extract the features of different foreground target and background sample image patches with relevant scales online, the sample patch having a maximal score is regarded as the new tracking result by classifying the compressive features via a naive bayesian classifier,meanwhile,to update the location and scale. Experimental results show the proposed algorithm performs favorably against state-of-the-art algorithms on challenging sequences in terms of the basic attitude and scale change, which is robust and does not depend on the scale selection of the initial tracking area.

scholarly journals Integrating Linguistic Knowledge to Sentence Paraphrase Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 8368-8375
Author(s):  
Zibo Lin ◽  
Ziran Li ◽  
Ning Ding ◽  
Hai-Tao Zheng ◽  
Ying Shen ◽  
...  
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Linguistic Knowledge ◽  
Position Information ◽  
Encoding Scheme ◽  
Human Evaluation ◽  
Task Architecture ◽  
The Given ◽  
Paraphrase Generation ◽  
Selection Of

Paraphrase generation aims to rewrite a text with different words while keeping the same meaning. Previous work performs the task based solely on the given dataset while ignoring the availability of external linguistic knowledge. However, it is intuitive that a model can generate more expressive and diverse paraphrase with the help of such knowledge. To fill this gap, we propose Knowledge-Enhanced Paraphrase Network (KEPN), a transformer-based framework that can leverage external linguistic knowledge to facilitate paraphrase generation. (1) The model integrates synonym information from the external linguistic knowledge into the paraphrase generator, which is used to guide the decision on whether to generate a new word or replace it with a synonym. (2) To locate the synonym pairs more accurately, we adopt an incremental encoding scheme to incorporate position information of each synonym. Besides, a multi-task architecture is designed to help the framework jointly learn the selection of synonym pairs and the generation of expressive paraphrase. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art approaches in terms of both automatic and human evaluation.

Sign in / Sign up

Export Citation Format

Share Document