scholarly journals Guided Networks for Few-Shot Image Segmentation and Fully Connected CRFs

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1508
Author(s):  
Kun Zhang ◽  
Yuanjie Zheng ◽  
Xiaobo Deng ◽  
Weikuan Jia ◽  
Jian Lian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Fusion ◽  
Object Segmentation ◽  
Conditional Random Field ◽  
High Dimensional ◽  
Learning Method ◽  
Deep Convolutional Neural Networks ◽  
Structured Output ◽  
Fully Connected ◽  
Task Representations

The goal of the few-shot learning method is to learn quickly from a low-data regime. Structured output tasks like segmentation are challenging for few-shot learning, due to their being high-dimensional and statistically dependent. For this problem, we propose improved guided networks and combine them with a fully connected conditional random field (CRF). The guided network extracts task representations from annotated support images through feature fusion to do fast, accurate inference on new unannotated query images. By bringing together few-shot learning methods and fully connected CRFs, our method can do accurate object segmentation by overcoming poor localization properties of deep convolutional neural networks and can quickly updating tasks, without further optimization, when faced with new data. Our guided network is at the forefront of accuracy for the terms of annotation volume and time.

scholarly journals BE-FNet: 3D Bounding Box Estimation Feature Pyramid Network for Accurate and Efficient Maxillary Sinus Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zhuofu Deng ◽  
Binbin Wang ◽  
Zhiliang Zhu
Keyword(s):  
Neural Networks ◽  
Maxillary Sinus ◽  
Convolutional Neural Networks ◽  
Feature Fusion ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
3D Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Bounding Box ◽  
Proposed Model

Maxillary sinus segmentation plays an important role in the choice of therapeutic strategies for nasal disease and treatment monitoring. Difficulties in traditional approaches deal with extremely heterogeneous intensity caused by lesions, abnormal anatomy structures, and blurring boundaries of cavity. 2D and 3D deep convolutional neural networks have grown popular in medical image segmentation due to utilization of large labeled datasets to learn discriminative features. However, for 3D segmentation in medical images, 2D networks are not competent in extracting more significant spacial features, and 3D ones suffer from unbearable burden of computation, which results in great challenges to maxillary sinus segmentation. In this paper, we propose a deep neural network with an end-to-end manner to generalize a fully automatic 3D segmentation. At first, our proposed model serves a symmetrical encoder-decoder architecture for multitask of bounding box estimation and in-region 3D segmentation, which cannot reduce excessive computation requirements but eliminate false positives remarkably, promoting 3D segmentation applied in 3D convolutional neural networks. In addition, an overestimation strategy is presented to avoid overfitting phenomena in conventional multitask networks. Meanwhile, we introduce residual dense blocks to increase the depth of the proposed network and attention excitation mechanism to improve the performance of bounding box estimation, both of which bring little influence to computation cost. Especially, the structure of multilevel feature fusion in the pyramid network strengthens the ability of identification to global and local discriminative features in foreground and background achieving more advanced segmentation results. At last, to address problems of blurring boundary and class imbalance in medical images, a hybrid loss function is designed for multiple tasks. To illustrate the strength of our proposed model, we evaluated it against the state-of-the-art methods. Our model performed better significantly with an average Dice 0.947±0.031, VOE 10.23±5.29, and ASD 2.86±2.11, respectively, which denotes a promising technique with strong robust in practice.

scholarly journals Convergence Behavior of DNNs with Mutual-Information-Based Regularization

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 727 ◽  
Author(s):  
Hlynur Jónsson ◽  
Giovanni Cherubini ◽  
Evangelos Eleftheriou
Keyword(s):  
Neural Networks ◽  
Mutual Information ◽  
Low Complexity ◽  
High Dimensional ◽  
Test Accuracy ◽  
Compression Phase ◽  
Hidden Layer ◽  
Low Dimensional ◽  
Fully Connected ◽  
Fully Connected Networks

Information theory concepts are leveraged with the goal of better understanding and improving Deep Neural Networks (DNNs). The information plane of neural networks describes the behavior during training of the mutual information at various depths between input/output and hidden-layer variables. Previous analysis revealed that most of the training epochs are spent on compressing the input, in some networks where finiteness of the mutual information can be established. However, the estimation of mutual information is nontrivial for high-dimensional continuous random variables. Therefore, the computation of the mutual information for DNNs and its visualization on the information plane mostly focused on low-complexity fully connected networks. In fact, even the existence of the compression phase in complex DNNs has been questioned and viewed as an open problem. In this paper, we present the convergence of mutual information on the information plane for a high-dimensional VGG-16 Convolutional Neural Network (CNN) by resorting to Mutual Information Neural Estimation (MINE), thus confirming and extending the results obtained with low-dimensional fully connected networks. Furthermore, we demonstrate the benefits of regularizing a network, especially for a large number of training epochs, by adopting mutual information estimates as additional terms in the loss function characteristic of the network. Experimental results show that the regularization stabilizes the test accuracy and significantly reduces its variance.

scholarly journals Not so fast: Limited validity of deep convolutional neural networks as in silico models for human naturalistic face processing

2021 ◽  
Author(s):  
Guo Jiahui ◽  
Ma Feilong ◽  
Matteo Visconti di Oleggio Castello ◽  
Samuel A Nastase ◽  
James V Haxby ◽  
...  
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Face Processing ◽  
In Silico ◽  
Processing System ◽  
Human Face ◽  
Deep Convolutional Neural Networks ◽  
Neural Representations ◽  
Fully Connected

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The relationships between internal representations learned by DCNNs and those of the primate face processing system are not well understood, especially in naturalistic settings. We developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces) and used representational similarity analysis to investigate how well the representations learned by high-performing DCNNs match human brain representations across the entire distributed face processing system. DCNN representational geometries were strikingly consistent across diverse architectures and captured meaningful variance among faces. Similarly, representational geometries throughout the human face network were highly consistent across subjects. Nonetheless, correlations between DCNN and neural representations were very weak overall—DCNNs captured 3% of variance in the neural representational geometries at best. Intermediate DCNN layers better matched visual and face-selective cortices than the final fully-connected layers. Behavioral ratings of face similarity were highly correlated with intermediate layers of DCNNs, but also failed to capture representational geometry in the human brain. Our results suggest that the correspondence between intermediate DCNN layers and neural representations of naturalistic human face processing is weak at best, and diverges even further in the later fully-connected layers. This poor correspondence can be attributed, at least in part, to the dynamic and cognitive information that plays an essential role in human face processing but is not modeled by DCNNs. These mismatches indicate that current DCNNs have limited validity as in silico models of dynamic, naturalistic face processing in humans.

scholarly journals Inferring Skin Lesion Segmentation With Fully Connected CRFs Based on Multiple Deep Convolutional Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 144246-144258
Author(s):  
Yuming Qiu ◽  
Jingyong Cai ◽  
Xiaolin Qin ◽  
Ju Zhang
Keyword(s):  
Neural Networks ◽  
Skin Lesion ◽  
Convolutional Neural Networks ◽  
Lesion Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Fully Connected

scholarly journals Scratch Detection in Cars Using Mask Region Convolution Neural Networks

2020 ◽  
Author(s):  
Vijay K ◽  
Vijayakumar R ◽  
Sivaranjani P ◽  
Logeshwari R
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Quality Control ◽  
Automobile Industry ◽  
Digital Imaging ◽  
High Dimensional ◽  
Image Processing Algorithm ◽  
Aesthetic Standard ◽  
Fully Connected

This task depends on quality control in the vehicle business. It centers on the imprint and harms in new cars before producing to the client. This project presents the development of a system of recognition of defects and cosmetic imperfections in cars. This application gives a quick and strong robotized results. It likewise gives framework acknowledgement of scratches. In the as of now existing framework, the way toward distinguishing the scratches in car is finished by our mankind. Using the input frames, sections of the vehicles are entered for training, the last Fully-connected layer is altered so that it only has two exit categories: Sections with scratches and without scratches. This project is mainly developed to minimize manpower and maximize automation on quality department in automobile industry. It is a computer vision project. It includes task such as acquiring, processing, analyzing and understanding digital images and extraction of high dimensional data. An image processing algorithm is used in order to manipulate an image to achieve an aesthetic standard and to provide a translation between the human visual system and digital imaging services.

scholarly journals Kayu7net: Identifikasi dan Evaluasi F-Measure Citra Kayu berbasis Deep Convolutional Neural Network (DCNN)

2020 ◽  
Vol 7 (6) ◽  
pp. 1089
Author(s):  
Iwan Muhammad Erwin ◽  
Risnandar Risnandar ◽  
Esa Prakarsa ◽  
Bambang Sugiarto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Iteration Process ◽  
Batch Size ◽  
Wood Identification ◽  
Deep Convolutional Neural Networks ◽  
Fully Connected ◽  
F Measure

<p class="Abstrak">Identifikasi kayu salah satu kebutuhan untuk mendukung pemerintah dan kalangan bisnis kayu untuk melakukan perdagangan kayu secara legal. Keahlian khusus dan waktu yang cukup dibutuhkan untuk memproses identifikasi kayu di laboratorium. Beberapa metodologi penelitian sebelumnya, proses identifikasi kayu masih dengan cara menggabungkan sistem manual menggunakan anatomi DNA kayu. Sedangkan penggunaan sistem komputer diperoleh dari citra penampamg melintang kayu secara proses mikrokopis dan makroskopis. Saat ini, telah berkembang teknologi computer vision dan machine learning untuk mengidentifikasi berbagai jenis objek, salah satunya citra kayu. Penelitian ini berkontribusi dalam mengklasifikasi beberapa spesies kayu yang diperdagangkan menggunakan Deep Convolutional Neural Networks (DCNN). Kebaruan penelitian ini terletak pada arsitektur DCNN yang bernama Kayu7Net. Arsitektur Kayu7Net yang diusulkan memiliki tiga lapisan konvolusi terhadap tujuh spesies dataset citra kayu. Pengujian dengan merubah citra input menjadi berukuran 600×600, 300×300, dan 128×128 piksel serta masing-masing diulang pada epoch 50 dan 100. DCNN yang diusulkan menggunakan fungsi aktivasi ReLU dengan batch size 32. ReLU bersifat lebih konvergen dan cepat saat proses iterasi. Sedangkan Fully-Connected (FC) berjumlah 4 lapisan akan menghasilkan proses training yang lebih efisien. Hasil eksperimen memperlihatkan bahwa Kayu7Net yang diusulkan memiliki nilai akurasi sebesar 95,54%, precision sebesar 95,99%, recall sebesar 95,54%, specificity sebesar 99,26% dan terakhir, nilai F-measure sebesar 95,46%. Hasil ini menunjukkan bahwa arsitektur Kayu7Net lebih unggul sebesar 1,49% pada akurasi, 2,49% pada precision, dan 5,26% pada specificity dibandingkan penelitian sebelumnya.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>Wood identification is one of the needs to support the government and the wood business community for a legally wood trading system. Special expertise and sufficient time are needed to process wood identification in the laboratory. Some previous research works show that the process of identifying wood combines a manual system using a wood DNA anatomy. While, the use of a computer system is obtained from the wood image of microscopic and macroscopic process. Recently, the latest technology has developed by using the machine learning and computer vision to identify many objects, the one of them is wood image. This research contributes to classify several the traded wood species by using Deep Convolutional Neural Networks (DCNN). The novelty of this research is in the DCNN architecture, namely Kayu7Net. The proposed of Kayu7Net Architecture has three convolution layers of the seven species wood image dataset. The testing changes the wood image input to 600×600, 300×300, and 128×128 pixel, respectively, and each of them repeated until 50 and 100 epoches, respectively. The proposed DCNN uses the ReLU activation function and batch size 32. The ReLU is more convergent and faster during the iteration process. Whereas, the 4 layers of Fully-Connected (FC) will produce a more efficient training process. The experimental results show that the proposed Kayu7Net has an accuracy value of 95.54%, a precision of 95.99%, a recall of 95.54%, a specificity of 99.26% and finally, an F-measure value of 95.46%. These results indicate that Kayu7Net is superior by 1.49% of accuracy, 2.49% of precision, and 5.26% of specificity compared to the previous work. </em></p><p class="Abstrak"> </p>

scholarly journals Analysis of deep learning methods for blind protein contact prediction in CASP12

2017 ◽  
Author(s):  
Sheng Wang ◽  
Siqi Sun ◽  
Jinbo Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Solvent Accessibility ◽  
Classification Problem ◽  
Learning Method ◽  
Deep Convolutional Neural Networks ◽  
Contact Potential ◽  
Contact Prediction ◽  
Average Accuracy ◽  
Structure Relationship

AbstractHere we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L=length). A more advanced implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as an image pixel-level labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and co-evolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both 1D and 2D deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method.

Disease Identification in Plant Leaf Using Deep Convolutional Neural Networks

2020 ◽  
pp. 46-62
Author(s):  
Venu K. ◽  
Natesan Palanisamy ◽  
Krishnakumar B. ◽  
Sasipriyaa N.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Early Detection ◽  
Economic Loss ◽  
Input Image ◽  
Deep Convolutional Neural Networks ◽  
Early Detection Of Disease ◽  
Disease Identification ◽  
Fully Connected

Early detection of disease in the plant leads to an early treatment and reduction in the economic loss considerably. Recent development has introduced deep learning based convolutional neural network for detecting the diseases in the images accurately using image classification techniques. In the chapter, CNN is supplied with the input image. In each convolutional layer of CNN, features are extracted and are transferred to the next pooling layer. Finally, all the features which are extracted from convolution layers are concatenated and formed as input to the fully-connected layer of state-of-the-art architecture and then output class will be predicted by the model. The model is evaluated for three different datasets such as grape, pepper, and peach leaves. It is observed from the experimental results that the accuracy of the model obtained for grape, pepper, peach datasets are 74%, 69%, 84%, respectively.

scholarly journals Deep Convolutional Neural Network With a Multi-Scale Attention Feature Fusion Module for Segmentation of Multimodal Brain Tumor

2021 ◽  
Vol 15 ◽  
Author(s):  
Xueqin He ◽  
Wenjie Xu ◽  
Jane Yang ◽  
Jianyao Mao ◽  
Sifang Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Deep Convolutional Neural Networks ◽  
Multi Scale ◽  
Mri Brain ◽  
Feature Information

As a non-invasive, low-cost medical imaging technology, magnetic resonance imaging (MRI) has become an important tool for brain tumor diagnosis. Many scholars have carried out some related researches on MRI brain tumor segmentation based on deep convolutional neural networks, and have achieved good performance. However, due to the large spatial and structural variability of brain tumors and low image contrast, the segmentation of MRI brain tumors is challenging. Deep convolutional neural networks often lead to the loss of low-level details as the network structure deepens, and they cannot effectively utilize the multi-scale feature information. Therefore, a deep convolutional neural network with a multi-scale attention feature fusion module (MAFF-ResUNet) is proposed to address them. The MAFF-ResUNet consists of a U-Net with residual connections and a MAFF module. The combination of residual connections and skip connections fully retain low-level detailed information and improve the global feature extraction capability of the encoding block. Besides, the MAFF module selectively extracts useful information from the multi-scale hybrid feature map based on the attention mechanism to optimize the features of each layer and makes full use of the complementary feature information of different scales. The experimental results on the BraTs 2019 MRI dataset show that the MAFF-ResUNet can learn the edge structure of brain tumors better and achieve high accuracy.

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