ArticleJournal of the American Medical Informatics Association : JAMIA
ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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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
27 citing papers in PubMed.
- EfficientNetB5-Based Deep Learning for Automated Cancer Detection in Tissue Microarray Images.Journal of Cancer · 2026Article
- Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence.Frontiers in digital health · 2022Review
- An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities.Journal of pathology informatics · 2022Article
- Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.Medical image analysis · 2021Article
- AI in Medical Imaging Informatics: Current Challenges and Future Directions.IEEE journal of biomedical and health informatics · 2020Article
- PRISM: A Platform for Imaging in Precision Medicine.JCO clinical cancer informatics · 2020Article
- Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches.Frontiers in neuroscience · 2020Article
- Article
- Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images.IEEE journal of biomedical and health informatics · 2019Article
- Innovative approaches for cancer treatment: current perspectives and new challenges.Ecancermedicalscience · 2019Article
- A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images.Cancer research · 2017Article
- Parallel multiple instance learning for extremely large histopathology image analysis.BMC bioinformatics · 2017Article
- An Assessment of Imaging Informatics for Precision Medicine in Cancer.Yearbook of medical informatics · 2017Review
- Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- A Methodology for Texture Feature-based Quality Assessment in Nucleus Segmentation of Histopathology Image.Journal of pathology informatics · 2017Article
- PATMA: parser of archival tissue microarray.PeerJ · 2016Article
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review.IEEE reviews in biomedical engineering · 2016Review
- Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies.BMC bioinformatics · 2015Article
- High-throughput histopathological image analysis via robust cell segmentation and hashing.Medical image analysis · 2015Article
- Parallel content-based sub-image retrieval using hierarchical searching.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
OBJECTIVE AND
designThe design and implementation of ImageMiner, a software platform for performing comparative analysis of expression patterns in imaged microscopy specimens such as tissue microarrays (TMAs), is described. ImageMiner is a federated system of services that provides a reliable set of analytical and data management capabilities for investigative research applications in pathology. It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support TMA analysis. The system is designed to leverage high-performance computing machines so that investigators can rapidly analyze large ensembles of imaged TMA specimens. To support deployment in collaborative, multi-institutional projects, ImageMiner features grid-enabled, service-based components so that multiple instances of ImageMiner can be accessed remotely and federated.
resultsThe experimental evaluation shows that: (1) ImageMiner is able to support reliable detection and feature extraction of tumor regions within imaged tissues; (2) images and analysis results managed in ImageMiner can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified tissue and TMA; and (3) the system is able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples.
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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.