scholarly journals Automated Ground Truth Generation for Learning-Based Crack Detection on Concrete Surfaces

2021 ◽  
Vol 11 (22) ◽  
pp. 10966
Author(s):  
Hsiang-Chieh Chen ◽  
Zheng-Ting Li
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Ground Truth ◽  
Experimental Results ◽  
Training Data ◽  
Generation Process ◽  
Detection Model ◽  
Ground Truth Generation ◽  
Labeling Approach ◽  
The Cost

This article introduces an automated data-labeling approach for generating crack ground truths (GTs) within concrete images. The main algorithm includes generating first-round GTs, pre-training a deep learning-based model, and generating second-round GTs. On the basis of the generated second-round GTs of the training data, a learning-based crack detection model can be trained in a self-supervised manner. The pre-trained deep learning-based model is effective for crack detection after it is re-trained using the second-round GTs. The main contribution of this study is the proposal of an automated GT generation process for training a crack detection model at the pixel level. Experimental results show that the second-round GTs are similar to manually marked labels. Accordingly, the cost of implementing learning-based methods is reduced significantly because data labeling by humans is not necessitated.

scholarly journals On the objectivity, reliability, and validity of deep learning enabled bioimage analyses

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Dennis Segebarth ◽  
Matthias Griebel ◽  
Nikolai Stein ◽  
Cora R von Collenberg ◽  
Corinna Martin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Biological Effects ◽  
Reliability And Validity ◽  
Ground Truth ◽  
Training Data ◽  
Model Organisms ◽  
Data Annotation ◽  
Bioimage Analysis ◽  
Model Training

Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.

scholarly journals Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhengqiao Zhao ◽  
Alexandru Cristian ◽  
Gail Rosen
Keyword(s):  
Incremental Learning ◽  
Current Knowledge ◽  
Bacterial Genome ◽  
Ground Truth ◽  
Training Data ◽  
Genome Database ◽  
Learning Classifier ◽  
The Rich ◽  
Efficient Learning ◽  
The Cost

Abstract Background It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of “incremental learning” addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data. Results We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model’s knowledge of genomes grows, classification accuracy increases. The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in 1/4th of the non-incremental time with no accuracy loss. Conclusions It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources.

Deep learning-driven velocity model building workflow

2019 ◽  
Vol 38 (11) ◽  
pp. 872a1-872a9 ◽  
Author(s):  
Mauricio Araya-Polo ◽  
Stuart Farris ◽  
Manuel Florez
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Model Building ◽  
Ground Truth ◽  
Velocity Model ◽  
Training Data ◽  
Quality Data ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Velocity Models

Exploration seismic data are heavily manipulated before human interpreters are able to extract meaningful information regarding subsurface structures. This manipulation adds modeling and human biases and is limited by methodological shortcomings. Alternatively, using seismic data directly is becoming possible thanks to deep learning (DL) techniques. A DL-based workflow is introduced that uses analog velocity models and realistic raw seismic waveforms as input and produces subsurface velocity models as output. When insufficient data are used for training, DL algorithms tend to overfit or fail. Gathering large amounts of labeled and standardized seismic data sets is not straightforward. This shortage of quality data is addressed by building a generative adversarial network (GAN) to augment the original training data set, which is then used by DL-driven seismic tomography as input. The DL tomographic operator predicts velocity models with high statistical and structural accuracy after being trained with GAN-generated velocity models. Beyond the field of exploration geophysics, the use of machine learning in earth science is challenged by the lack of labeled data or properly interpreted ground truth, since we seldom know what truly exists beneath the earth's surface. The unsupervised approach (using GANs to generate labeled data)illustrates a way to mitigate this problem and opens geology, geophysics, and planetary sciences to more DL applications.

scholarly journals Deep-Learning-Based Cerebral Artery Semantic Segmentation in Neurosurgical Operating Microscope Vision Using Indocyanine Green Fluorescence Videoangiography

2022 ◽  
Vol 15 ◽  
Author(s):  
Min-seok Kim ◽  
Joon Hyuk Cha ◽  
Seonhwa Lee ◽  
Lihong Han ◽  
Wonhyoung Park ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Indocyanine Green ◽  
Blood Vessels ◽  
Cerebral Artery ◽  
Ground Truth ◽  
Neural Network Models ◽  
Indocyanine Green Fluorescence ◽  
Surgical Assistance ◽  
Ground Truth Generation

There have been few anatomical structure segmentation studies using deep learning. Numbers of training and ground truth images applied were small and the accuracies of which were low or inconsistent. For a surgical video anatomy analysis, various obstacles, including a variable fast-changing view, large deformations, occlusions, low illumination, and inadequate focus occur. In addition, it is difficult and costly to obtain a large and accurate dataset on operational video anatomical structures, including arteries. In this study, we investigated cerebral artery segmentation using an automatic ground-truth generation method. Indocyanine green (ICG) fluorescence intraoperative cerebral videoangiography was used to create a ground-truth dataset mainly for cerebral arteries and partly for cerebral blood vessels, including veins. Four different neural network models were trained using the dataset and compared. Before augmentation, 35,975 training images and 11,266 validation images were used. After augmentation, 260,499 training and 90,129 validation images were used. A Dice score of 79% for cerebral artery segmentation was achieved using the DeepLabv3+ model trained using an automatically generated dataset. Strict validation in different patient groups was conducted. Arteries were also discerned from the veins using the ICG videoangiography phase. We achieved fair accuracy, which demonstrated the appropriateness of the methodology. This study proved the feasibility of operating field view of the cerebral artery segmentation using deep learning, and the effectiveness of the automatic blood vessel ground truth generation method using ICG fluorescence videoangiography. Using this method, computer vision can discern blood vessels and arteries from veins in a neurosurgical microscope field of view. Thus, this technique is essential for neurosurgical field vessel anatomy-based navigation. In addition, surgical assistance, safety, and autonomous surgery neurorobotics that can detect or manipulate cerebral vessels would require computer vision to identify blood vessels and arteries.

scholarly journals DEEP LEARNING FOR 3D RECONSTRUCTION OF THE MARTIAN SURFACE USING MONOCULAR IMAGES: A FIRST GLANCE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1111-1116
Author(s):  
Z. Chen ◽  
B. Wu ◽  
W. C. Liu
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Experimental Results ◽  
Training Data ◽  
Network System ◽  
Stereo Images ◽  
Martian Surface ◽  
Preliminary Results ◽  
Low Level ◽  
Traditional Technology

Abstract. The paper presents our efforts on CNN-based 3D reconstruction of the Martian surface using monocular images. The Viking colorized global mosaic and Mar Express HRSC blended DEM are used as training data. An encoder-decoder network system is employed in the framework. The encoder section extracts features from the images, which includes convolution layers and reduction layers. The decoder section consists of deconvolution layers and is to integrate features and convert the images to desired DEMs. In addition, skip connection between encoder and decoder section is applied, which offers more low-level features for the decoder section to improve its performance. Monocular Context Camera (CTX) images are used to test and verify the performance of the proposed CNN-based approach. Experimental results show promising performances of the proposed approach. Features in images are well utilized, and topographical details in images are successfully recovered in the DEMs. In most cases, the geometric accuracies of the generated DEMs are comparable to those generated by the traditional technology of photogrammetry using stereo images. The preliminary results show that the proposed CNN-based approach has great potential for 3D reconstruction of the Martian surface.

scholarly journals Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

2019 ◽  
Vol 11 (11) ◽  
pp. 1309 ◽  
Author(s):  
Ben G. Weinstein ◽  
Sergio Marconi ◽  
Stephanie Bohlman ◽  
Alina Zare ◽  
Ethan White
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Training Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Detection Model ◽  
High Resolution Imagery ◽  
The Neural Network ◽  
Detection Approach ◽  
Crown Delineation

Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in RGB imagery while using a semi-supervised deep learning detection network. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. We show that deep learning models can leverage existing Light Detection and Ranging (LIDAR)-based unsupervised delineation to generate trees that are used for training an initial RGB crown detection model. Despite limitations in the original unsupervised detection approach, this noisy training data may contain information from which the neural network can learn initial tree features. We then refine the initial model using a small number of higher-quality hand-annotated RGB images. We validate our proposed approach while using an open-canopy site in the National Ecological Observation Network. Our results show that a model using 434,551 self-generated trees with the addition of 2848 hand-annotated trees yields accurate predictions in natural landscapes. Using an intersection-over-union threshold of 0.5, the full model had an average tree crown recall of 0.69, with a precision of 0.61 for the visually-annotated data. The model had an average tree detection rate of 0.82 for the field collected stems. The addition of a small number of hand-annotated trees improved the performance over the initial self-supervised model. This semi-supervised deep learning approach demonstrates that remote sensing can overcome a lack of labeled training data by generating noisy data for initial training using unsupervised methods and retraining the resulting models with high quality labeled data.

scholarly journals EVICAN—a balanced dataset for algorithm development in cell and nucleus segmentation

2020 ◽  
Vol 36 (12) ◽  
pp. 3863-3870
Author(s):  
Mischa Schwendy ◽  
Ronald E Unger ◽  
Sapun H Parekh
Keyword(s):  
Deep Learning ◽  
Cell Biology ◽  
Ground Truth ◽  
Training Data ◽  
Training Dataset ◽  
Visual Cell ◽  
Application Development ◽  
Ground Truth Data ◽  
Quantitative Image ◽  
Nucleus Segmentation

Abstract Motivation Deep learning use for quantitative image analysis is exponentially increasing. However, training accurate, widely deployable deep learning algorithms requires a plethora of annotated (ground truth) data. Image collections must contain not only thousands of images to provide sufficient example objects (i.e. cells), but also contain an adequate degree of image heterogeneity. Results We present a new dataset, EVICAN—Expert visual cell annotation, comprising partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms and magnifications that is readily usable as training data for computer vision applications. With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development. Availability and implementation The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=). Using a Mask R-CNN implementation, we demonstrate automated segmentation of cells and nuclei from brightfield images with a mean average precision of 61.6 % at a Jaccard Index above 0.5.

Neural Supermodeling

2020 ◽  
Author(s):  
Wim Wiegerinck
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Model Performance ◽  
Ground Truth ◽  
Training Data ◽  
Combination Method ◽  
Neural Network Approach ◽  
Modeling Approach ◽  
Promising Tool ◽  
Spatially Extended

<p>Deep learning is a modeling approach that has shown impressive results in image processing and is arguably a promising tool for dealing with spatially extended complex systems such earth atmosphere with its visually interpretable patterns. A disadvantage of the neural network approach is that it typically requires an enormous amount of training data.</p><p> </p><p>Another recently proposed modeling approach is supermodeling. In supermodeling it is assumed that a dynamical system – the truth – is modelled by a set of good but imperfect models. The idea is to improve model performance by dynamically combining imperfect models during the simulation. The resulting combination of models is called the supermodel. The combination strength has to be learned from data. However, since supermodels do not start from scratch, but make use of existing domain knowledge, they may learn from less data.</p><p> </p><p>One of the ways to combine models is to define the tendencies of the supermodel as linear (weighted) combinations of the imperfect model tendencies. Several methods including linear regression have been proposed to optimize the weights.  However, the combination method might also be nonlinear. In this work we propose and explore a novel combination of deep learning and supermodeling, in which convolutional neural networks are used as tool to combine the predictions of the imperfect models.  The different supermodeling strategies are applied in simulations in a controlled environment with a three-level, quasi-geostrophic spectral model that serves as ground truth and perturbed models that serve as the imperfect models.</p>

scholarly journals Learning unsupervised contextual representations for medical synonym discovery

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 538-546
Author(s):  
Elliot Schumacher ◽  
Mark Dredze
Keyword(s):  
Deep Learning ◽  
Cerebrovascular Accident ◽  
Contextual Information ◽  
Training Data ◽  
Medical Terminology ◽  
Medical Texts ◽  
Neural Representations ◽  
Broad Variety ◽  
The Cost ◽  
Discovery Method

Abstract Objectives An important component of processing medical texts is the identification of synonymous words or phrases. Synonyms can inform learned representations of patients or improve linking mentioned concepts to medical ontologies. However, medical synonyms can be lexically similar (“dilated RA” and “dilated RV”) or dissimilar (“cerebrovascular accident” and “stroke”); contextual information can determine if 2 strings are synonymous. Medical professionals utilize extensive variation of medical terminology, often not evidenced in structured medical resources. Therefore, the ability to discover synonyms, especially without reliance on training data, is an important component in processing training notes. The ability to discover synonyms from models trained on large amounts of unannotated data removes the need to rely on annotated pairs of similar words. Models relying solely on non-annotated data can be trained on a wider variety of texts without the cost of annotation, and thus may capture a broader variety of language. Materials and Methods Recent contextualized deep learning representation models, such as ELMo (Peters et al., 2019) and BERT, (Devlin et al. 2019) have shown strong improvements over previous approaches in a broad variety of tasks. We leverage these contextualized deep learning models to build representations of synonyms, which integrate the context of surrounding sentence and use character-level models to alleviate out-of-vocabulary issues. Using these models, we perform unsupervised discovery of likely synonym matches, which reduces the reliance on expensive training data. Results We use the ShARe/CLEF eHealth Evaluation Lab 2013 Task 1b data to evaluate our synonym discovery method. Comparing our proposed contextualized deep learning representations to previous non-neural representations, we find that the contextualized representations show consistent improvement over non-contextualized models in all metrics. Conclusions Our results show that contextualized models produce effective representations for synonym discovery. We expect that the use of these representations in other tasks would produce similar gains in performance.

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