ArticleAlgorithms2009
Computer-Aided Diagnosis in Mammography Using Content-based Image Retrieval Approaches: Current Status and Future Perspectives.
Article in Algorithms, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
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Who cites it
13 citing papers in PubMed.
- Deep Attention Fusion Hashing (DAFH) Model for Medical Image Retrieval.Bioengineering (Basel, Switzerland) · 2024Article
- Evaluation of a Novel Content-Based Image Retrieval System for the Differentiation of Interstitial Lung Diseases in CT Examinations.Diagnostics (Basel, Switzerland) · 2021Article
- A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.Journal of X-ray science and technology · 2017Article
- Assessment of performance and reproducibility of applying a content-based image retrieval scheme for classification of breast lesions.Medical physics · 2015Article
- A CAD System for Hemorrhagic Stroke.The neuroradiology journal · 2014Article
- Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use.Medical physics · 2013Article
- Thyroid nodule recognition based on feature selection and pixel classification methods.Journal of digital imaging · 2013Article
- Towards a repository for standardized medical image and signal case data annotated with ground truth.Journal of digital imaging · 2012Article
- Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer.Medical physics · 2012Article
- Regularization in retrieval-driven classification of clustered microcalcifications for breast cancer.International journal of biomedical imaging · 2012Article
- Assessment of performance and reliability of computer-aided detection scheme using content-based image retrieval approach and limited reference database.Journal of digital imaging · 2011Article
- Exploring the potential of context-sensitive CADe in screening mammography.Medical physics · 2010Article
- Analysis of human brain by magnetic resonance imaging using content-based image retrieval.International journal of health sciencesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
As the rapid advance of digital imaging technologies, the content-based image retrieval (CBIR) has became one of the most vivid research areas in computer vision. In the last several years, developing computer-aided detection and/or diagnosis (CAD) schemes that use CBIR to search for the clinically relevant and visually similar medical images (or regions) depicting suspicious lesions has also been attracting research interest. CBIR-based CAD schemes have potential to provide radiologists with "visual aid" and increase their confidence in accepting CAD-cued results in the decision making. The CAD performance and reliability depends on a number of factors including the optimization of lesion segmentation, feature selection, reference database size, computational efficiency, and relationship between the clinical relevance and visual similarity of the CAD results. By presenting and comparing a number of approaches commonly used in previous studies, this article identifies and discusses the optimal approaches in developing CBIR-based CAD schemes and assessing their performance. Although preliminary studies have suggested that using CBIR-based CAD schemes might improve radiologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage. Much research work is needed before the CBIR-based CAD schemes can be accepted in the clinical practice.
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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.