scholarly journals A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hans Liebl ◽  
David Schinz ◽  
Anjany Sekuboyina ◽  
Luca Malagutti ◽  
Maximilian T. Löffler ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Large Scale ◽  
Automated Analysis ◽  
Anatomical Variations ◽  
Training Dataset ◽  
Segmentation Algorithms ◽  
Labelling Information ◽  
Vertebrae Segmentation ◽  
Radiological Image

AbstractWith the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent automated analysis of morphology and pathology. The first “Large Scale Vertebrae Segmentation Challenge” (VerSe 2019) showed that these perform well on normal anatomy, but fail in variants not frequently present in the training dataset. Building on that experience, we report on the largely increased VerSe 2020 dataset and results from the second iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020 comprises annotated spine computed tomography (CT) images from 300 subjects with 4142 fully visualized and annotated vertebrae, collected across multiple centres from four different scanner manufacturers, enriched with cases that exhibit anatomical variants such as enumeration abnormalities (n = 77) and transitional vertebrae (n = 161). Metadata includes vertebral labelling information, voxel-level segmentation masks obtained with a human-machine hybrid algorithm and anatomical ratings, to enable the development and benchmarking of robust and accurate segmentation algorithms.

scholarly journals A Fortran-Keras Deep Learning Bridge for Scientific Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Jordan Ott ◽  
Mike Pritchard ◽  
Natalie Best ◽  
Erik Linstead ◽  
Milan Curcic ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Climate Model ◽  
Emergent Behavior ◽  
Training Dataset ◽  
Climate Simulation ◽  
Network Architectures ◽  
Efficient Computation ◽  
Radiation Physics

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset.

scholarly journals Deep Learning Model Improves Radiologists’ Performance in Detection and Classification of Breast Lesions

2021 ◽  
Author(s):  
Ying-Shi Sun ◽  
Yu-Hong Qu ◽  
Dong Wang ◽  
Yi Li ◽  
Lin Ye ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Roc Curve ◽  
False Positive ◽  
Large Scale ◽  
False Positive Rate ◽  
Training Dataset ◽  
Validation Dataset ◽  
Breast Lesions ◽  
Positive Rate

Abstract Background: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application.Methods: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, retrospectively collected mammograms from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists’ performance with and without it. Finally, prospectively multicenter mammograms were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve.Results: The sensitivity of model for detecting lesion after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign from malignant lesions was 0.855 (95% CI: 0.830, 0.880). The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.808, P = 0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P = 0.03). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, PPV, and NPV of 94.36%, 98.07%, 87.76%, and 99.09%, respectively.Conclusions: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.Trial registration: NCT, NCT03708978. Registered 17 April 2018, https://register.clinicaltrials.gov/prs/app/ NCT03708978

scholarly journals Automated analysis of rabbit knee calcified cartilage morphology using micro‐computed tomography and deep learning

2021 ◽  
Author(s):  
Santeri J. O. Rytky ◽  
Lingwei Huang ◽  
Petri Tanska ◽  
Aleksei Tiulpin ◽  
Egor Panfilov ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automated Analysis ◽  
Micro Computed Tomography ◽  
Rabbit Knee ◽  
Calcified Cartilage

scholarly journals The Practicality of Deep Learning Algorithms in COVID-19 Detection: Application to Chest X-ray Images

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 183
Author(s):  
Abdulaziz Alorf
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Personnel ◽  
Automated Analysis ◽  
Main Method ◽  
X Ray ◽  
The World ◽  
Chest X Ray ◽  
Trained Physician

Since January 2020, the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected the whole world, producing a respiratory disease that can become severe and even cause death in certain groups of people. The main method for diagnosing coronavirus disease 2019 (COVID-19) is performing viral tests. However, the kits for carrying out these tests are scarce in certain regions of the world. Lung conditions as perceived in computed tomography and radiography images exhibit a high correlation with the presence of COVID-19 infections. This work attempted to assess the feasibility of using convolutional neural networks for the analysis of pulmonary radiography images to distinguish COVID-19 infections from non-infected cases and other types of viral or bacterial pulmonary conditions. The results obtained indicate that these networks can successfully distinguish the pulmonary radiographies of COVID-19-infected patients from radiographies that exhibit other or no pathology, with a sensitivity of 100% and specificity of 97.6%. This could help future efforts to automate the process of identifying lung radiography images of suspicious cases, thereby supporting medical personnel when many patients need to be rapidly checked. The automated analysis of pulmonary radiography is not intended to be a substitute for formal viral tests or formal diagnosis by a properly trained physician but rather to assist with identification when the need arises.

scholarly journals Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery

2021 ◽  
Vol 11 (19) ◽  
pp. 9180
Author(s):  
Siangruei Wu ◽  
Yihong Wu ◽  
Haoyun Chang ◽  
Florence T. Su ◽  
Hengchun Liao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sampling Method ◽  
State Of The Art ◽  
Pituitary Tumors ◽  
Semantic Segmentation ◽  
Brain Lesions ◽  
Training Dataset ◽  
Training Set ◽  
Segmentation Algorithms ◽  
Method Model

Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.

scholarly journals Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection

2020 ◽  
Vol 101 (11) ◽  
pp. E1980-E1995 ◽  
Author(s):  
Stephan Rasp ◽  
Hauke Schulz ◽  
Sandrine Bony ◽  
Bjorn Stevens
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Climate Models ◽  
Training Dataset ◽  
Trade Wind ◽  
Radiation Balance ◽  
Shallow Convection ◽  
Wide Range ◽  
Shallow Clouds ◽  
New Phenomena

AbstractHumans excel at detecting interesting patterns in images, for example, those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowdsourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earth’s radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish, and Gravel. On cloud-labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowdsourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggest promising research questions. Further, this study illustrates that crowdsourcing and deep learning complement each other well for the exploration of image datasets.

scholarly journals Learning from Synthetic Dataset for Crop Seed Instance Segmentation

2019 ◽  
Author(s):  
Yosuke Toda ◽  
Fumio Okura ◽  
Jun Ito ◽  
Satoshi Okada ◽  
Toshinori Kinoshita ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Synthetic Data ◽  
Seed Morphology ◽  
Training Data ◽  
Training Dataset ◽  
Labor Costs ◽  
Data Annotation ◽  
Instance Segmentation

Incorporating deep learning in the image analysis pipeline has opened the possibility of introducing precision phenotyping in the field of agriculture. However, to train the neural network, a sufficient amount of training data must be prepared, which requires a time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. After training with such a dataset, performance based on recall and the average Precision of the real-world test dataset achieved 96% and 95%, respectively. Applying our pipeline enables extraction of morphological parameters at a large scale, enabling precise characterization of the natural variation of barley from a multivariate perspective. Importantly, we show that our approach is effective not only for barley seeds but also for various crops including rice, lettuce, oat, and wheat, and thus supporting the fact that the performance benefits of this technique is generic. We propose that constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs needed to prepare the training dataset for deep learning in the agricultural domain.

scholarly journals Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

2020 ◽  
Vol 34 (04) ◽  
pp. 6372-6379
Author(s):  
Bingzhe Wu ◽  
Chaochao Chen ◽  
Shiwan Zhao ◽  
Cen Chen ◽  
Yuan Yao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Information Leakage ◽  
Langevin Dynamics ◽  
Stochastic Gradient ◽  
Training Dataset ◽  
Mcmc Methods ◽  
Convergence Properties ◽  
Generalization Bounds ◽  
Real World Applications

Bayesian deep learning is recently regarded as an intrinsic way to characterize the weight uncertainty of deep neural networks (DNNs). Stochastic Gradient Langevin Dynamics (SGLD) is an effective method to enable Bayesian deep learning on large-scale datasets. Previous theoretical studies have shown various appealing properties of SGLD, ranging from the convergence properties to the generalization bounds. In this paper, we study the properties of SGLD from a novel perspective of membership privacy protection (i.e., preventing the membership attack). The membership attack, which aims to determine whether a specific sample is used for training a given DNN model, has emerged as a common threat against deep learning algorithms. To this end, we build a theoretical framework to analyze the information leakage (w.r.t. the training dataset) of a model trained using SGLD. Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent. Moreover, our theoretical analysis can be naturally extended to other types of Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods. Empirical results on different datasets and models verify our theoretical findings and suggest that the SGLD algorithm can not only reduce the information leakage but also improve the generalization ability of the DNN models in real-world applications.

scholarly journals A DEEP LEARNING APPROACH FOR URBAN UNDERGROUND OBJECTS DETECTION FROM VEHICLE-BORNE GROUND PENETRATING RADAR DATA IN REAL-TIME

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 293-299
Author(s):  
Z. Zong ◽  
C. Chen ◽  
X. Mi ◽  
W. Sun ◽  
Y. Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Ground Penetrating Radar ◽  
Large Scale ◽  
Test Site ◽  
Training Dataset ◽  
Underground Space ◽  
Buried Objects ◽  
Real Time Detection ◽  
Ground Penetrating

<p><strong>Abstract.</strong> GPRs (Ground Penetrating Radar) are widely adopted in underground space survey and mapping, because of their advantages of fast data acquisition, convenience, high imaging resolution and NDT (Non Destructive Testing) inspection. However, at present, the automation of the GPR data post-processing is low and the identification of underground objects needs expert interpretation. The heavy manual interpretation labor limits the GPR applications in large-scale urban scenarios. According to the latest research, it is still an unsolved problem to detect targets or defects in GPR data automatically and needs further exploration. In this paper, we propose a deep learning method for real-time detection of underground targets from GPR data. Seven typical targets in urban underground space are identified and labelled to construct the training dataset. The constructed dataset is consist of 489 labelled samples including rainwater wells, cables, metal/nonmetal pipes, sparse/dense steel reinforcement, voids. The training dataset is further augmented to produce more samples. DarkNet53 convolutional neural network (CNN) is trained using the constructed training dataset including realistic data and augmented data to extract features of the buried objects. And then the end-to-end YOLO detection framework is used to classify and locate the seven specific categories buried targets in the GPR data in real time. Experiments show that the automatic real-time detection method proposed in this paper can effectively detect the buried objects in the ground penetrating radar image in real time at Shenzhen test site (typical urban road scene).</p>

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