ArticleInternational journal of biomedical imaging2012
Pixel-based machine learning in medical imaging.
Article in International journal of biomedical imaging, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Machine learning evaluation of pneumonia severity: subgroup performance in the Medical Imaging and Data Resource Center modified radiographic assessment of lung edema mastermind challenge.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.BJR open · 2024Review
- Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach.Scientific reports · 2023Article
- An update in musculoskeletal tumors: from quantitative imaging to radiomics.La Radiologia medica · 2021Review
- Automated Segmentation of Abnormal Tissues in Medical Images.Journal of biomedical physics & engineering · 2021Article
- Artificial Intelligence for Response Evaluation With PET/CT.Seminars in nuclear medicine · 2021Review
- The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up.Frontiers in oncology · 2021Review
- Findings from machine learning in clinical medical imaging applications - Lessons for translation to the forensic setting.Forensic science international · 2020Review
- The detection of lung cancer using massive artificial neural network based on soft tissue technique.BMC medical informatics and decision making · 2020Article
- Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.Journal of applied clinical medical physics · 2020Article
- Machine and deep learning methods for radiomics.Medical physics · 2020Review
- Graph-based description of tertiary lymphoid organs at single-cell level.PLoS computational biology · 2020Article
- Deep Learning in CT Images: Automated Pulmonary Nodule Detection for Subsequent Management Using Convolutional Neural Network.Cancer management and research · 2020Article
- Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography.Journal of Korean Neurosurgical Society · 2019Article
- Data Engineering for Machine Learning in Women's Imaging and Beyond.AJR. American journal of roentgenology · 2019Article
- Machine learning concepts, concerns and opportunities for a pediatric radiologist.Pediatric radiology · 2019Review
- Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks.BMC bioinformatics · 2018Article
- Overview of deep learning in medical imaging.Radiological physics and technology · 2017Review
- Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.IEICE transactions on information and systems · 2013Article
- Computer-aided diagnosis systems for lung cancer: challenges and methodologies.International journal of biomedical imaging · 2013Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, 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 classification of objects such as lesions into certain classes (e.g., abnormal or normal, or lesions or nonlesions) based on input features (e.g., contrast and circularity) obtained from segmented object candidates. Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which use pixel/voxel values in images directly instead of features calculated from segmented objects as input information; thus, feature calculation or segmentation is not required. Because the PML can avoid errors caused by inaccurate feature calculation and segmentation which often occur for subtle or complex objects, the performance of the PML can potentially be higher for such objects than that of common classifiers (i.e., feature-based MLs). In this paper, PMLs are surveyed to make clear (a) classes of PMLs, (b) similarities and differences within (among) different PMLs and those between PMLs and feature-based MLs, (c) advantages and limitations of PMLs, and (d) their applications in medical imaging.
Identifiers
What OpenQuestion holds
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.