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RadCloud—An Artificial Intelligence-Based Research Platform Integrating Machine Learning-Based Radiomics, Deep Learning, and Data Management
2021
Journal of Artificial Intelligence for Medical Sciences
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For an individual project, annotation can be performed from different devices and stored securely, while a database is shared to make research/communication in a common annotation environment for a collaborative ...
U-net based deep CNN models integrated in DL module have been used for automatic segmentation of Type B aortic dissection (TBAD) from computed tomography angiography (CTA) [33] . ...
doi:10.2991/jaims.d.210617.001
fatcat:u55jqsbwmjh53croyv5wm2wxba
Auxiliary Image Regularization for Deep CNNs with Noisy Labels
[article]
2016
arXiv
pre-print
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In this work, we consider the problem of training a deep CNN model for image classification with mislabeled training samples - an issue that is common in real image data sets with tags supplied by amateur ...
mutual context information among training images and encourages the model to select reliable images to robustify the learning process. ...
Intuitively, the proposed regularizer exploits the structure of the data and automatically retrieves useful auxiliary examples to collaboratively facilitate training of the classification model. ...
arXiv:1511.07069v2
fatcat:kuhuhyikrjcvvejec65umkl2xi
The need for training and benchmark datasets for convolutional neural networks in flood applications
2022
Hydrology Research
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We note the lack of open and labelled flood image datasets and the growing need for an open, benchmark data library for image classification, object detection, and segmentation relevant to flood management ...
Flood-related image datasets from social media, smartphones, CCTV cameras, and unmanned aerial vehicles (UAVs) present valuable data for the management of flood risk, and particularly for the application ...
images for community collaboration. ...
doi:10.2166/nh.2022.093
fatcat:h3ct5oiyvba6hikumst6toh6gi
State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods
2021
Cancers
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This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016–2021. ...
Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. ...
Acknowledgments: Authors would like to thank National Key R&D Program of China for providing experimental facilities to conduct this study. ...
doi:10.3390/cancers13215546
pmid:34771708
pmcid:PMC8583666
fatcat:3xavsdok7zdp5oa2ix2gtkolbq
Deep Learning Based Obstacle Awareness from Airborne Optical Sensors
2023
Journal of the American Helicopter Society
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We describe our experimental setup comprising the CNN+SVM model and datasets of predefined classes of obstacles—pylons, chimneys, antennas, TV towers, wind turbines, helicopters—synthesized from prerecorded ...
for many civil operators. ...
Combining CNN and SVM for Automatic Obstacle Detection/Classification In this section, we present key, high-level requirements for the automatic obstacle detection/classification problem and introduce ...
doi:10.4050/jahs.68.042012
fatcat:f7so7xgxerb4rbfoxtdjphwsiq
Multimodal Social Media Analysis for Gang Violence Prevention
[article]
2018
arXiv
pre-print
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To this end, we developed a rigorous methodology for collecting and annotating tweets. ...
We gathered 1,851 tweets and accompanying annotations related to visual concepts and the psychosocial codes: aggression, loss, and substance use. ...
Furthermore, we thank all our annotators: Allison Aguilar, Rebecca Carlson, Natalie Hession, Chloe Martin, Mirinda Morency. ...
arXiv:1807.08465v1
fatcat:2svgtswq4ndxhhyisi3xbfs4ya
Joint regression and learning from pairwise rankings for personalized image aesthetic assessment
2021
Computational Visual Media
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for the same image. ...
We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train ...
Acknowledgements The authors thank the reviewers for their valuable comments. This ...
doi:10.1007/s41095-021-0207-y
fatcat:yve552ozqbahzh66bhkl6tn3lu
Detection of Possible Illicit Messages Using Natural Language Processing and Computer Vision on Twitter and Linked Websites
2020
IEEE Access
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INDEX TERMS CNN, features detection, image classification, natural language processing, SVM. 44534 This work is licensed under a Creative Commons Attribution 4.0 License. ...
By applying Support Vector Machine (SVM) and Convolutional Neural Network (CNN), we are able to recognize gender and age group, taking into account torso information and its proportional relationship with ...
ACKNOWLEDGMENT The authors would like to thank the Escuela Politécnica Nacional for developing Seed Research Project PIS-17-10 ''Data Mining, Feature Vector Extraction, and Modeling with Pattern Recognition ...
doi:10.1109/access.2020.2976530
fatcat:6mcf34og4nhjxdbjfz6mq7q3ai
Multimodal Social Media Analysis for Gang Violence Prevention
2019
Proceedings of the ... International AAAI Conference on Weblogs and Social Media
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To this end, we developed a rigorous methodology for collecting and annotating tweets. ...
The annotated dataset will be made available for research with strong ethical protection mechanism. ...
Furthermore, we would like to thank the reviewers for their valuable feedback. ...
doi:10.1609/icwsm.v13i01.3214
fatcat:5rrinnza65dpzdfociq4ua2qye
Automatic Hierarchical Classification of Kelps Using Deep Residual Features
2020
Sensors
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Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. ...
We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. ...
Acknowledgments: The authors acknowledge NVIDIA for providing a Titan-X GPU for the experiments involved in this research.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20020447
pmid:31941132
pmcid:PMC7013955
fatcat:nscdhzkqmvd6lcdj2yx3du3j3a
Hierarchical Classification of Kelps utilizing Deep Residual Features
[article]
2019
arXiv
pre-print
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2020 accepted arXiv:1906.10881v2 fatcat:yejlskpnnzaebi7wjgab35nsuu
This paper presents an automatic hierarchical classification method to classify kelps from images collected by autonomous underwater vehicles. ...
Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. ...
Acknowledgments: The authors acknowledge NVIDIA for providing a Titan-X GPU for the experiments involved in this research.
Conflicts of Interest: The authors declare no conflict of interest. ...
arXiv:1906.10881v1
fatcat:73effc276fhknmogdzoiipjhse
Kusk Object Dataset: Recording access to objects in food preparation
2016
2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
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The data are obtained by manual annotation and by automatic processing. As a result of annotation, we collected 4391 object images from 57 cooking observations. ...
The records of access to object are known as a key evidence for understanding chef's activity in food preparation. In the dataset, we provide object images as well as the records of access to object. ...
Acknowledgement This work was supported by JSPS KAKENHI Grant Numbers 24240030, 26280084 and 16K16099. ...
doi:10.1109/icmew.2016.7574771
dblp:conf/icmcs/HashimotoMIM16
fatcat:b6qx27k2jzhgtixedfxfzwghxq
DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity
2021
Diagnostics
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It extracts significant features by first obtaining spatial features from the four convolution neural networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform ...
Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. ...
Figure 4 indicates that the AUC for L-SVM and Q-SVM is 0.98. ...
doi:10.3390/diagnostics11112034
pmid:34829380
pmcid:PMC8620568
fatcat:7sht7znivzho3fnlcb6jjf2ycm
Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment
[article]
2017
arXiv
pre-print
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Nazr-CNN attains promising results both for object detection and damage assessment suggesting that the integrated pipeline is robust in the face of small data sets and labeling errors by annotators. ...
We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. ...
Data Description The UAV image data and corresponding damage annotations were acquired as part of an initiative by the World Bank in collaboration with the Humanitarian UAV Network (UAViators) during Cyclone ...
arXiv:1611.06474v2
fatcat:jh7lgi62jfgdbkga5mqtvmqrwe
CELEBRITY IMAGE CLASSIFICATION
2022
International Journal of Engineering Applied Sciences and Technology
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t— Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labelled example photos. ...
The main motto of the project is to build a website where user can drag and drop an image of the celebrity and the website will provide the details of the celebrity. ...
Another framework named Hyper-class augmented and regularized deep learning [11] . This exposes the CNNs models to additional variations without the cost of collecting and annotating more data. ...
doi:10.33564/ijeast.2022.v06i09.025
fatcat:fg6m3zkmgza3lbnzmjwpywj4t4
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