A Collaborative Privacy-Preserving Deep Learning System in Distributed Mobile Environment

2016 ◽  
Author(s):  
Menghan Liu ◽  
Haotian Jiang ◽  
Jia Chen ◽  
Alaa Badokhon ◽  
Xuetao Wei ◽  
...  
Keyword(s):  
Deep Learning ◽  
Privacy Preserving ◽  
Learning System ◽  
Mobile Environment

scholarly journals Privacy-Preserving Distributed Deep Learning via Homomorphic Re-Encryption

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 411 ◽  
Author(s):  
Fengyi Tang ◽  
Wei Wu ◽  
Jian Liu ◽  
Huimei Wang ◽  
Ming Xian
Keyword(s):  
Deep Learning ◽  
Data Privacy ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Learning System ◽  
Stochastic Gradient Descent ◽  
Work Related ◽  
Learning Scheme ◽  
Security Properties ◽  
Secure Channels

The flourishing deep learning on distributed training datasets arouses worry about data privacy. The recent work related to privacy-preserving distributed deep learning is based on the assumption that the server and any learning participant do not collude. Once they collude, the server could decrypt and get data of all learning participants. Moreover, since the private keys of all learning participants are the same, a learning participant must connect to the server via a distinct TLS/SSL secure channel to avoid leaking data to other learning participants. To fix these problems, we propose a privacy-preserving distributed deep learning scheme with the following improvements: (1) no information is leaked to the server even if any learning participant colludes with the server; (2) learning participants do not need different secure channels to communicate with the server; and (3) the deep learning model accuracy is higher. We achieve them by introducing a key transform server and using homomorphic re-encryption in asynchronous stochastic gradient descent applied to deep learning. We show that our scheme adds tolerable communication cost to the deep learning system, but achieves more security properties. The computational cost of learning participants is similar. Overall, our scheme is a more secure and more accurate deep learning scheme for distributed learning participants.

scholarly journals Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

scholarly journals A deep-learning system to classify lung X-ray images into normal/pneumonia class

2020 ◽  
Vol 101 ◽  
pp. 209
Author(s):  
R. Baskaran ◽  
B. Ajay Rajasekaran ◽  
V. Rajinikanth
Keyword(s):  
Deep Learning ◽  
Learning System ◽  
X Ray

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽  
2020 ◽  
Author(s):  
Alanna Ebigbo ◽  
Robert Mendel ◽  
Tobias Rückert ◽  
Laurin Schuster ◽  
Andreas Probst ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pilot Study ◽  
White Light ◽  
Submucosal Invasion ◽  
Learning System ◽  
Endoscopic Images ◽  
Barrett’S Cancer ◽  
Significant Difference ◽  
Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs

Bone ◽  
2021 ◽  
pp. 115972
Author(s):  
Abhinav Suri ◽  
Brandon C. Jones ◽  
Grace Ng ◽  
Nancy Anabaraonye ◽  
Patrick Beyrer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intervertebral Discs ◽  
Learning System ◽  
Vertebral Bodies

Towards Underwater Sustainability using ROV Equipped with Deep Learning System

2020 ◽  
Author(s):  
Yi-Chia Wu ◽  
Po-Yen Shih ◽  
Li-Perng Chen ◽  
Chia-Chin Wang ◽  
Hooman Samani
Keyword(s):  
Deep Learning ◽  
Learning System
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