Integration of Convolutional Neural Networks for Pulmonary Nodule Malignancy Assessment in a Lung Cancer Classification Pipeline
Ilaria Bonavita,Xavier Rafael-Palou,Mario Ceresa,Gemma Piella,Vicent J. Ribas,Miguel Ángel González Ballester +5 more
TLDR
This article proposes to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung cancer detection by integrating predictive models of nodulemalignancy into a limited size lung cancer datasets.About:
This article is published in Computer Methods and Programs in Biomedicine.The article was published on 2020-03-01 and is currently open access. It has received 52 citations till now. The article focuses on the topics: Malignancy & Lung cancer.read more
Citations
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Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images
TL;DR: A 2D convolutional neural network (2D CNN) with Taguchi parametric optimization for automatically recognizing lung cancer from CT images is proposed, proving the superiority of proposed model.
Journal ArticleDOI
A multi-task CNN approach for lung nodule malignancy classification and characterization
Sónia Marques,Filippo Schiavo,Carlos Abreu Ferreira,João Pedrosa,Antonio José Ledo Alves da Cunha,Aurélio Campilho +5 more
TL;DR: It is shown that models that combine direct malignancy prediction with specific branches for nodule characterization have a better performance than the remaining models, achieving an Area Under the Curve of 0.783.
Journal ArticleDOI
A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans
TL;DR: A comprehensive review of the application and development of CAD systems is presented, covering public datasets of lung CT scans, commonly used evaluation metrics and various medical competitions, and the advantages of CNNs over traditional image processing methods are summarized.
Journal ArticleDOI
Deep Q-networks with web-based survey data for simulating lung cancer intervention prediction and assessment in the elderly: a quantitative study
Songjing Chen,Si Zhu Wu +1 more
TL;DR: In this paper , the authors employed Deep Q-Networks (DQN) to predict lung cancer optimal intervention strategy and assess intervention effect in aged 65 years and older (the elderly).
Journal ArticleDOI
Re-Identification and growth detection of pulmonary nodules without image registration using 3D siamese neural networks
Xavier Rafael-Palou,Anton Aubanell,Ilaria Bonavita,Mario Ceresa,Gemma Piella,Vicent J. Ribas,Miguel Ángel González Ballester +6 more
TL;DR: A novel method based on a 3D siamese neural network is presented, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
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