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Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
[article]
2018
arXiv
pre-print
Preserved Fulltext
in medical imaging. ...
These new models in medical image computing have important applications that form clinically relevant and very challenging unsupervised learning problems. ...
Unsupervised learning and generative modeling have numerous clinically relevant applications in medical image computing. ...
arXiv:1806.05452v1
fatcat:lvsgxhds3fathidazfjr63bgcq
Real-world attack on MTCNN face detection system
[article]
2019
arXiv
pre-print
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It was shown then that some deep learning-based face detectors are prone to adversarial attacks not only in a digital domain but also in the real world. ...
Our approach is capable of breaking the MTCNN detector in a real-world scenario. ...
Adversarial attacks in the real-world domain might impose more challenging problems as an attacker can mislead the network in a non-intrusive fashion. ...
arXiv:1910.06261v1
fatcat:n6egdqkyynfsrk63zqlnqsmxlm
Deployment of Artificial Intelligence in Real-World Practice
2020
Asia-Pacific Journal of Ophthalmology
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Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many image-driven clinical disciplines, including ophthalmology. ...
in the performance and accuracy of automated diagnoses that primarily focus on image recognition and feature detection. ...
The vulnerability of DL models has spurred the community to seriously rethink the security and robustness of AI in real-world deployments, especially in the medical domain. 84 Scaling up AI systems for ...
doi:10.1097/apo.0000000000000301
fatcat:kixrl4jrlvbhphxw3sodikj2aa
Real World Object Detection Dataset For Quadcopter Unmanned Aerial Vehicle Detection
2020
IEEE Access
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In order to maximize the effectiveness of the model, real world footage was utilized, transformed into images and hand-labelled to create a custom set of 56821 images and 55539 bounding boxes. ...
The dataset was divided into train and test subsets for further processing and used to generate 603 easily deployable Haar Cascades as well as 819 high performing Deep Neural Networks based models. ...
Therefore, another real world image dataset, called ImageNet, was created in 2009 [13] . ...
doi:10.1109/access.2020.3026192
fatcat:vmoqwtcydrh7zhqfzfqn7kk6ti
OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings
[article]
2021
arXiv
pre-print
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We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. ...
Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. ...
To tackle uncertainty and complexity in real-world settings, we propose an open, dynamic machine learning framework ( Fig. ...
arXiv:2109.04004v1
fatcat:7xzl5ejt4jcrpmiqfcbx4micj4
Real World Learning: Simulation and Gaming
[chapter]
2020
Applied Pedagogies for Higher Education
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Their role in real world learning is evaluated with reference to the benefits and challenges of their use for teaching and learning in Higher Education. ...
Lean, Moizer, Derham, Strachan and Bhuiyan aim to evaluate the role of simulations and games in real world learning. ...
Therefore, they can be considered an important link between theories espoused in the lecture hall and the real world. ...
doi:10.1007/978-3-030-46951-1_9
fatcat:mg3ci5uqpbcujfhbzt5oijjh3e
Deep learning from "passive feeding" to "selective eating" of real-world data
2020
npj Digital Medicine
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Our work demonstrates that "selective eating" of real-world data is necessary and needs to be considered in the development of image-based AI systems. ...
Here, using 40,562 UWF images, we develop a deep learning–based image filtering system (DLIFS) for detecting and filtering out poor-quality images in an automated fashion such that only good-quality images ...
ACKNOWLEDGEMENTS This study received funding from the National Key R&D Program of China (grant no. 2018YFC0116500), the National Natural Science Foundation of China (grant no. ...
doi:10.1038/s41746-020-00350-y
pmid:33145439
pmcid:PMC7603327
fatcat:arm52bvgmrafzia6m3vyyb7g44
Adversarial Machine Learning: Attacks From Laboratories to the Real World
2021
Computer
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Consider, for example, an automatic surveillance camera that uses certain ML algorithms. The system monitors people entering and leaving a building in real time. ...
A person wearing a special T-shirt walks by the building, but the camera does not detect the person's presence, as the T-shirt has a special pattern that effectively conceals the person from the camera ...
invisible to the targeted lidar detector system. › Real-world model stealing attacks: The imitation of real-world machine translation production systems from Google, Bing, and Systran constitutes real-world ...
doi:10.1109/mc.2021.3057686
fatcat:l2ibixsvoze3tartgl5bsaaq5u
Medical Information Mart for Intensive Care: A Foundation for the Fusion of Artificial Intelligence and Real-World Data
2021
Frontiers in Artificial Intelligence
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CONCLUSION MIMIC is an ICU real-world data set that serves as a research catalyst fusing the AI and ML efforts of technologists, academic researchers, regulators, and clinicians together to improve the ...
An example of the way MIMIC facilitates the regulatory mission involves the antidiarrheal medication Loperamide. ...
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). ...
doi:10.3389/frai.2021.691626
pmid:34136802
pmcid:PMC8201087
fatcat:hvaz7nwgzfgxzd3vi743jttfoi
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
[article]
2018
arXiv
pre-print
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We also show how privacy-safe training techniques can be used to reduce the overhead of encrypted inference for real-world datasets by leveraging transfer learning and differential privacy. ...
This work increases the viability of deep learning systems that use homomorphic encryption to protect user privacy. ...
However, state-of-the-art real world applications for deep learning like medical imaging applications commonly use modern very deep neural networks. ...
arXiv:1811.09953v1
fatcat:67vqy4vwqvahvd6kqcxuzp23ki
Scaling Out-of-Distribution Detection for Real-World Settings
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:1911.11132v3 fatcat:obsy3ko4bfdu7jkexi2b4pjogeOther Versions
To make future work in real-world settings possible, we create new benchmarks for three large-scale settings. ...
To set the stage for more realistic out-of-distribution detection, we depart from small-scale settings and explore large-scale multiclass and multi-label settings with high-resolution images and thousands ...
StreetHazards contains a highly diverse array of anomalies; BDD-Anomaly contains anomalies in real-world images. ...
arXiv:1911.11132v4
fatcat:6d235jsui5cjbciufvbxn65vcy
DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment
[article]
2020
arXiv
pre-print
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Deep neural networks (DNNs) are being widely applied for various real-world applications across domains due to their high performance (e.g., high accuracy on image classification). ...
We conduct a large-scale evaluation with fifteen degradation factors that may happen in the real world and compare with four state-of-the-art data augmentation methods and two DNN repairing methods, demonstrating ...
in the real world operational environment can behave erroneously and deviate from what they are designed for. ...
arXiv:2011.09884v1
fatcat:orurpxhbzzhzpg5mqrymo7fwli
Privacy Leakage of Real-World Vertical Federated Learning
[article]
2021
arXiv
pre-print
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In this paper, we consider an honest-but-curious adversary who participants in training a distributed ML model, does not deviate from the defined learning protocol, but attempts to infer private training ...
Generic federated learning has been studied extensively, and several learning protocols, as well as open-source frameworks, have been developed. ...
sum attack against the real-world implementation of SecureBoost, provided by FATE. ...
arXiv:2011.09290v2
fatcat:izxk2vmlvngyfpmeuqxz3evwma
Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator
2021
Frontiers in Computer Science
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challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. ...
The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of ...
The
based on Real World Data. ...
doi:10.3389/fcomp.2021.768266
fatcat:h73b7l6va5azxmgkcydwlqhadi
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
[article]
2022
arXiv
pre-print
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Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and time-consuming. ...
Developing natural language processing (NLP) methods for structuring RWD is thus essential for scaling real-world evidence generation. ...
INTRODUCTION Electronic medical records (EMRs) offer an unprecedented opportunity to harness real-world data (RWD) for accelerating progress in clinical research and care [22] . ...
arXiv:2203.10442v1
fatcat:btvencpymrfmhmai7nmurybt4u
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