scholarly journals Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification

2019 ◽  
Vol 33 ◽  
pp. 5829-5836 ◽  
Author(s):  
Yingxue Zhang ◽  
Soumyasundar Pal ◽  
Mark Coates ◽  
Deniz Ustebay
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Matrix Completion ◽  
Ground Truth ◽  
Graph Classification ◽  
Bayesian Formulation ◽  
Modelling Assumptions ◽  
Graph Parameters ◽  
Almost All ◽  
Block Models

Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion. Although the performance has been impressive, the current implementations have limited capability to incorporate uncertainty in the graph structure. Almost all GCNNs process a graph as though it is a ground-truth depiction of the relationship between nodes, but often the graphs employed in applications are themselves derived from noisy data or modelling assumptions. Spurious edges may be included; other edges may be missing between nodes that have very strong relationships. In this paper we adopt a Bayesian approach, viewing the observed graph as a realization from a parametric family of random graphs. We then target inference of the joint posterior of the random graph parameters and the node (or graph) labels. We present the Bayesian GCNN framework and develop an iterative learning procedure for the case of assortative mixed-membership stochastic block models. We present the results of experiments that demonstrate that the Bayesian formulation can provide better performance when there are very few labels available during the training process.

Graph classification based on structural features of significant nodes and spatial convolutional neural networks

2021 ◽  
Vol 423 ◽  
pp. 639-650
Author(s):  
Tinghuai Ma ◽  
Hongmei Wang ◽  
Lejun Zhang ◽  
Yuan Tian ◽  
Najla Al-Nabhan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Structural Features ◽  
Graph Classification

scholarly journals DeepInterface: Protein-protein interface validation using 3D Convolutional Neural Networks

2019 ◽  
Author(s):  
A.T. Balci ◽  
C. Gumeli ◽  
A. Hakouz ◽  
D. Yuret ◽  
O. Keskin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Protein Interactions ◽  
Data Bank ◽  
Validation Dataset ◽  
Data Sets ◽  
Protein Protein Interactions ◽  
3 Dimensional ◽  
Almost All ◽  
The Given

AbstractMotivationProtein–protein interactions are crucial in almost all biological processes. Proteins interact through their interfaces. It is important to determine how proteins interact through interfaces to understand protein binding mechanisms and to predict new protein-protein interactions.ResultsWe present DeepInterface, a deep learning based method which predicts, for a given protein complex, if the interface between the proteins of a complex is a true interface or not. The model is a 3-dimensional convolutional neural networks model and the positive datasets are obtained from all complexes in the Protein Data Bank, the negative datasets are the incorrect solutions of the docking decoys. The model analyzes a given interface structure and outputs the probability of the given structure being an interface. The accuracy of the model for several interface data sets, including PIFACE, PPI4DOCK, DOCKGROUND is approximately 88% in the validation dataset and 75% in the test dataset. The method can be used to improve the accuracy of template based PPI predictions.

scholarly journals Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

2020 ◽  
Vol 27 (4) ◽  
pp. 20-33
Author(s):  
Paulo César Pereira Júnior ◽  
Alexandre Monteiro ◽  
Rafael Da Luz Ribeiro ◽  
Antonio Carlos Sobieranski ◽  
Aldo Von Wangenheim
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Precision Agriculture ◽  
Ground Truth ◽  
Aerial Images ◽  
Weed Mapping ◽  
Classical Models ◽  
Classical Computer ◽  
Better Than

In this paper, we present a comparison between convolutional neural networks and classicalcomputer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models

scholarly journals Automatic onset detection using convolutional neural networks

2019 ◽  
Author(s):  
Willy Cornelissen ◽  
Maurício Loureiro
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Ground Truth ◽  
Onset Detection ◽  
Ground Truth Data ◽  
Music Transcription ◽  
Automatic Music Transcription ◽  
Information Research ◽  
Music Research ◽  
Music Information

A very significant task for music research is to estimate instants when meaningful events begin (onset) and when they end (offset). Onset detection is widely applied in many fields: electrocardiograms, seismographic data, stock market results and many Music Information Research(MIR) tasks, such as Automatic Music Transcription, Rhythm Detection, Speech Recognition, etc. Automatic Onset Detection(AOD) received, recently, a huge contribution coming from Artificial Intelligence (AI) methods, mainly Machine Learning and Deep Learning. In this work, the use of Convolutional Neural Networks (CNN) is explored by adapting its original architecture in order to apply the approach to automatic onset detection on audio musical signals. We used a CNN network for onset detection on a very general dataset, well acknowledged by the MIR community, and examined the accuracy of the method by comparison to ground truth data published by the dataset. The results are promising and outperform another methods of musical onset detection.

scholarly journals Using Convolutional Neural Networks for Detection and Morphometric Analysis of Carolina Bays from Publicly Available Digital Elevation Models

2021 ◽  
Vol 13 (18) ◽  
pp. 3770
Author(s):  
Mark A. Lundine ◽  
Arthur C. Trembanis
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Feature Detection ◽  
Digital Elevation Models ◽  
Open Water ◽  
Atlantic Coastal Plain ◽  
Detection Scheme ◽  
Carolina Bays ◽  
Digital Elevation ◽  
Almost All

Carolina Bays are oriented and sandy-rimmed depressions that are ubiquitous throughout the Atlantic Coastal Plain (ACP). Their origin has been a highly debated topic since the 1800s and remains unsolved. Past population estimates of Carolina Bays have varied vastly, ranging between as few as 10,000 to as many as 500,000. With such a large uncertainty around the actual population size, mapping these enigmatic features is a problem that requires an automated detection scheme. Using publicly available LiDAR-derived digital elevation models (DEMs) of the ACP as training images, various types of convolutional neural networks (CNNs) were trained to detect Carolina bays. The detection results were assessed for accuracy and scalability, as well as analyzed for various morphologic, land-use and land cover, and hydrologic characteristics. Overall, the detector found over 23,000 Carolina Bays from southern New Jersey to northern Florida, with highest densities along interfluves. Carolina Bays in Delmarva were found to be smaller and shallower than Bays in the southeastern ACP. At least a third of Carolina Bays have been converted to agricultural lands and almost half of all Carolina Bays are forested. Few Carolina Bays are classified as open water basins, yet almost all of the detected Bays were within 2 km of a water body. In addition, field investigations based upon detection results were performed to describe the sedimentology of Carolina Bays. Sedimentological investigations showed that Bays typically have 1.5 m to 2.5 m thick sand rims that show a gradient in texture, with coarser sand at the bottom and finer sand and silt towards the top. Their basins were found to be 0.5 m to 2 m thick and showed a mix of clayey, silty, and sandy deposits. Last, the results compiled during this study were compared to similar depressional features (i.e., playa-lunette systems) to pinpoint any similarities in origin processes. Altogether, this study shows that CNNs are valuable tools for automated geomorphic feature detection and can lead to new insights when coupled with various forms of remotely sensed and field-based datasets.

scholarly journals Water Level Estimation in Sewer Pipes Using Deep Convolutional Neural Networks

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3412
Author(s):  
Joakim Bruslund Haurum ◽  
Chris H. Bahnsen ◽  
Malte Pedersen ◽  
Thomas B. Moeslund
Keyword(s):  
Neural Networks ◽  
Water Level ◽  
Convolutional Neural Networks ◽  
Contextual Information ◽  
Ground Truth ◽  
Water Levels ◽  
Deep Convolutional Neural Networks ◽  
Regression Problem ◽  
Still Images ◽  
Visual Appearance

Sewer pipe inspections are currently conducted by professionals who remotely control a robot from above ground. This expensive and slow approach is prone to human mistakes. Therefore, there is both an economic and scientific interest in automating the inspection process by creating systems able to recognize sewer defects. However, the extent of research put into automatic water level estimation in sewers has been limited despite being a prerequisite for further analysis of the pipe as only sections above the water level can be visually inspected. In this work, we utilize a dataset of still images obtained from over 5000 inspections carried out for three different Danish water utilities companies. This dataset is used for training and testing decision tree methods and convolutional neural networks (CNNs) for automatic water level estimation. We pose the estimation problem as a classification and regression problem, and compare the results of both approaches. Furthermore, we compare the effect of using different inspection standards for labeling the ground truth water level. By treating the problem as a classification task and using the 2015 Danish sewer inspection standard, where water levels are clustered based on visual appearance, we achieve an averaged F1 score of 79.29% using a fine-tuned ResNet-50 CNN. This shows the potential of using CNNs for water level estimation. We believe including temporal and contextual information will improve the results further.

scholarly journals Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

2020 ◽  
Vol 15 (10) ◽  
pp. 1445-1454 ◽  
Author(s):  
Giulia Ligabue ◽  
Federico Pollastri ◽  
Francesco Fontana ◽  
Marco Leonelli ◽  
Luciana Furci ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Kidney Biopsy ◽  
Ground Truth ◽  
Light Chains ◽  
Direct Immunofluorescence ◽  
Support Tool ◽  
Glomerular Deposits ◽  
Web Platform

Background and objectivesImmunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neural network as a support tool for kidney immunofluorescence reporting.Design, setting, participants, & measurementsHigh-magnification (×400) immunofluorescence images of kidney biopsies performed from the year 2001 to 2018 were collected. The report, adopted at the Division of Nephrology of the AOU Policlinico di Modena, describes the specimen in terms of “appearance,” “distribution,” “location,” and “intensity” of the glomerular deposits identified with fluorescent antibodies against IgG, IgA, IgM, C1q and C3 complement fractions, fibrinogen, and κ- and λ-light chains. The report was used as ground truth for the training of the convolutional neural networks.ResultsIn total, 12,259 immunofluorescence images of 2542 subjects undergoing kidney biopsy were collected. The test set analysis showed accuracy values between 0.79 (“irregular capillary wall” feature) and 0.94 (“fine granular” feature). The agreement test of the results obtained by the convolutional neural networks with respect to the ground truth showed similar values to three pathologists of our center. Convolutional neural networks were 117 times faster than human evaluators in analyzing 180 test images. A web platform, where it is possible to upload digitized images of immunofluorescence specimens, is available to evaluate the potential of our approach.ConclusionsThe data showed that the accuracy of convolutional neural networks is comparable with that of pathologists experienced in the field.

scholarly journals Building realistic structure models to train convolutional neural networks for seismic structural interpretation

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. WA27-WA39 ◽  
Author(s):  
Xinming Wu ◽  
Zhicheng Geng ◽  
Yunzhi Shi ◽  
Nam Pham ◽  
Sergey Fomel ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Structural Information ◽  
Ground Truth ◽  
Structural Features ◽  
Image Features ◽  
Training Data ◽  
Structural Interpretation ◽  
Seismic Image ◽  
Seismic Images

Seismic structural interpretation involves highlighting and extracting faults and horizons that are apparent as geometric features in a seismic image. Although seismic image processing methods have been proposed to automate fault and horizon interpretation, each of which today still requires significant human effort. We improve automatic structural interpretation in seismic images by using convolutional neural networks (CNNs) that recently have shown excellent performances in detecting and extracting useful image features and objects. The main limitation of applying CNNs in seismic interpretation is the preparation of many training data sets and especially the corresponding geologic labels. Manually labeling geologic features in a seismic image is highly time-consuming and subjective, which often results in incompletely or inaccurately labeled training images. To solve this problem, we have developed a workflow to automatically build diverse structure models with realistic folding and faulting features. In this workflow, with some assumptions about typical folding and faulting patterns, we simulate structural features in a 3D model by using a set of parameters. By randomly choosing the parameters from some predefined ranges, we are able to automatically generate numerous structure models with realistic and diverse structural features. Based on these structure models with known structural information, we further automatically create numerous synthetic seismic images and the corresponding ground truth of structural labels to train CNNs for structural interpretation in field seismic images. Accurate results of structural interpretation in multiple field seismic images indicate that our workflow simulates realistic and generalized structure models from which the CNNs effectively learn to recognize real structures in field images.

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