Journal ArticleDOI
Semi-automated detection of anterior cruciate ligament injury from MRI
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TLDR
Experimental results suggest potential clinical application of computer-aided decision making, both for detecting milder injuries and detecting complete ruptures in a human knee.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2017-03-01. It has received 53 citations till now. The article focuses on the topics: ACL injury & Anterior cruciate ligament.read more
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Journal ArticleDOI
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.
Nicholas Bien,Pranav Rajpurkar,Robyn L. Ball,Jeremy Irvin,Allison Park,Erik Jones,Michael Bereket,Bhavik N. Patel,Kristen W. Yeom,Katie Shpanskaya,Safwan Halabi,Evan J. Zucker,Gary S. Fanton,Derek F. Amanatullah,Christopher F. Beaulieu,Geoffrey M. Riley,Russell Stewart,Francis G. Blankenberg,David B. Larson,Ricky Jones,Curtis P. Langlotz,Andrew Y. Ng,Matthew P. Lungren +22 more
TL;DR: A deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams is developed and the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation is supported.
Journal ArticleDOI
Machine Learning in Orthopedics: A Literature Review.
TL;DR: A systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose is presented.
Journal ArticleDOI
Deep Learning for Detection of Complete Anterior Cruciate Ligament Tear.
TL;DR: A customized 3D deep learning architecture based on dynamic patch-based sampling demonstrates high performance in detection of complete ACL tears with over 96% test set accuracy.
Journal ArticleDOI
Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach.
Mazhar Javed Awan,Mazhar Javed Awan,Mohd Shafry Mohd Rahim,Naomie Salim,Mazin Abed Mohammed,Begonya Garcia-Zapirain,Karrar Hameed Abdulkareem +6 more
TL;DR: In this article, the authors used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Related Papers (5)
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.
Nicholas Bien,Pranav Rajpurkar,Robyn L. Ball,Jeremy Irvin,Allison Park,Erik Jones,Michael Bereket,Bhavik N. Patel,Kristen W. Yeom,Katie Shpanskaya,Safwan Halabi,Evan J. Zucker,Gary S. Fanton,Derek F. Amanatullah,Christopher F. Beaulieu,Geoffrey M. Riley,Russell Stewart,Francis G. Blankenberg,David B. Larson,Ricky Jones,Curtis P. Langlotz,Andrew Y. Ng,Matthew P. Lungren +22 more