ArticleIEICE transactions on information and systems2013
Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.
Article in IEICE transactions on information and systems, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 44 citations in OpenAlex.
- Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients.SN computer science · 2023Article
- Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging.Frontiers in neuroinformatics · 2022Review
- [Artificial intelligence-based algorithms : Decision-making support for computed tomography of the chest].Der Radiologe · 2020Review
- Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network.Cancer management and research · 2020Article
- Overview of deep learning in medical imaging.Radiological physics and technology · 2017Review
- Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.Radiological physics and technology · 2017Review
- Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data.Journal of clinical monitoring and computing · 2016Article
- Progress in Fully Automated Abdominal CT Interpretation.AJR. American journal of roentgenology · 2016Review
- Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.Critical care medicine · 2016Observational
- Three-dimensional SVM with latent variable: application for detection of lung lesions in CT images.Journal of medical systems · 2015Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine "optimal" boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.