scholarly journals Development of Automated Angiogram Labeling via AI Active Learning Pipeline

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Kolten Kersey ◽  
Andrew Gonzalez
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Blood Vessels ◽  
Sensitivity Threshold ◽  
Initial Image ◽  
Automated Learning ◽  
First Pass ◽  
Tissue Contrast ◽  
Internal Iliac ◽  
Image Set

Background and Objective:  As technology is integrated further into medicine, more specialties are discovering new uses for it in their clinical practice. However, the tasks that we want technology to complete are often removed from developer’s intended tasks.  A field of research is growing that integrates medicine with current AI technology to bridge the gap and utilize already existing technology for medical uses.  We desire to use an active learning pipeline (a form of machine learning) to automate the labeling of blood vessels on angiograms and potentially develop the ability to detect occlusions. By using machine learning, it would essentially allow the machine to teach itself with human guidance.      Methods:  A machine learning pipeline is in development for automation of the process.  To create a baseline for the machine to start learning, the first set of angiograms are being labeled by hand using the program 3D Slicer.  For the first pass, we have been quickly labeling the blood vessels by changing the color sensitivity threshold to highlight the darker blood vessels juxtaposed next to lighter tissue.  For the second pass, we have erased any erroneous highlighting that was picked up in the first pass such as tools, tissue, contrast outside the injection site, and sutures.  For the third pass, we have labeled and segmented the arteries into specific vessels such as femoral, common iliac, internal iliac, etc. This will then be entered into the machine for automated learning.    Results:  We are in the process of labeling the initial image set.      Potential Impact:   By creating a lab for angiogram automation, it will allow physicians to efficiently search images for specific arteries and save valuable time usually spent searching images.  This would also allow for automated labeling of occlusions that a physician could then look at to verify.     

scholarly journals AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection

2020 ◽  
Vol 34 (04) ◽  
pp. 3537-3544
Author(s):  
Xu Chen ◽  
Brett Wujek
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Learning System ◽  
Automated Learning ◽  
Benchmark Datasets ◽  
Hyperparameter Selection ◽  
Query Selection

Automated machine learning (AutoML) strives to establish an appropriate machine learning model for any dataset automatically with minimal human intervention. Although extensive research has been conducted on AutoML, most of it has focused on supervised learning. Research of automated semi-supervised learning and active learning algorithms is still limited. Implementation becomes more challenging when the algorithm is designed for a distributed computing environment. With this as motivation, we propose a novel automated learning system for distributed active learning (AutoDAL) to address these challenges. First, automated graph-based semi-supervised learning is conducted by aggregating the proposed cost functions from different compute nodes in a distributed manner. Subsequently, automated active learning is addressed by jointly optimizing hyperparameters in both the classification and query selection stages leveraging the graph loss minimization and entropy regularization. Moreover, we propose an efficient distributed active learning algorithm which is scalable for big data by first partitioning the unlabeled data and replicating the labeled data to different worker nodes in the classification stage, and then aggregating the data in the controller in the query selection stage. The proposed AutoDAL algorithm is applied to multiple benchmark datasets and a real-world electrocardiogram (ECG) dataset for classification. We demonstrate that the proposed AutoDAL algorithm is capable of achieving significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.

scholarly journals How to measure uncertainty in uncertainty sampling for active learning

2021 ◽  
Author(s):  
Vu-Linh Nguyen ◽  
Mohammad Hossein Shaker ◽  
Eyke Hüllermeier
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Sampling Strategies ◽  
Total Uncertainty ◽  
Uncertainty Sampling ◽  
Different Types ◽  
Alternative Approaches ◽  
Active Learner ◽  
Probabilistic Nature ◽  
Different Sources

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

Preoperative evaluation of iliac blood vessels for first kidney transplant recipients: Combination of conventional and contrast-enhanced ultrasonography

2021 ◽  
pp. 1-11
Author(s):  
Wen-qi Yang ◽  
Xiao-lan Cui ◽  
Ming Zhang ◽  
Xiao-dong Yuan ◽  
Liang Ying ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Iliac Artery ◽  
Internal Iliac Artery ◽  
Operative Strategy ◽  
Post Transplant ◽  
Transplant Outcomes ◽  
Contrast Enhanced ◽  
Internal Iliac ◽  
Contrast Enhanced Ultrasonography ◽  
Us Findings

OBJECTIVE: To assess iliac blood vessels using conventional ultrasound (US) and contrast-enhanced ultrasonography (CEUS) before kidney transplantation (KT) and determine whether US findings related to post-transplant outcomes. METHODS: A total of 119 patients received US and CEUS before KT waiting-list acceptance. The preoperative iliac blood hemodynamics and vascular conditions were evaluated. The operative strategy and follow-up outcomes were recorded. Logistic regression and correlation analysis were used. The accuracy in determining the patency of iliac blood vessels was calculated before and after the injection of contrast materials. RESULTS: CEUS can help to significantly improve the visualization of the internal iliac artery, but there was no significant correlation with post-transplant outcomes. In terms of accuracy, there were significant differences in determining the patency of internal iliac arteries between conventional US and CEUS (60.5% and 100%, p <  0.001). The surgical strategy of one patient was regulated and two patients were excluded from KT according to US findings. CONCLUSIONS: Compared with conventional US, CEUS helps to improve the visualization of the internal iliac artery. Conventional US and CEUS have the potential to serve as effective methods to evaluate anatomy and hemodynamics of iliac vessels and have a potential value while defining clinical algorithms in surgery decision-making.

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

A Machine Learning Approach to First Pass Reperfusion in Mechanical Thrombectomy: Prediction and Feature Analysis

2021 ◽  
Vol 30 (7) ◽  
pp. 105796
Author(s):  
Lohit Velagapudi ◽  
Nikolaos Mouchtouris ◽  
Richard F. Schmidt ◽  
David Vuong ◽  
Omaditya Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Thrombectomy ◽  
Feature Analysis ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
First Pass

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal ◽  
Alvaro Vidal Torreira
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Design Optimization ◽  
Fuel Injection ◽  
A Priori ◽  
Computational Cost ◽  
Learning Approach ◽  
Data Generation ◽  
Compression Ignition Engine ◽  
Hyperparameter Selection

AbstractThe use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Efficiency of Fast Approximation Analysis by Simulation and Machine Learning Using Active Learning

2021 ◽  
Vol 2021 (0) ◽  
pp. 606
Author(s):  
Ryushi MINETA ◽  
Yoshiharu IWATA ◽  
Hidefumi WAKAMATU ◽  
Yuya MATSUMOTO ◽  
Toshiki KAWAMURA
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Approximation Analysis

Headnote Prediction Using Machine Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sarmad Mahar ◽  
Sahar Zafar ◽  
Kamran Nishat
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active Learning ◽  
Text Classification ◽  
Extraction Methods ◽  
Text Summarization ◽  
Training Data ◽  
Second Step ◽  
Support Vector ◽  
Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

scholarly journals IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6743
Author(s):  
Vasiliki Kelli ◽  
Vasileios Argyriou ◽  
Thomas Lagkas ◽  
George Fragulis ◽  
Elisavet Grigoriou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Network Flow ◽  
Detection System ◽  
Human Life ◽  
Industrial Sector ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Learning Approaches ◽  
Monitoring And Control

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.

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