ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2012
Digital Pathology: Data-Intensive Frontier in Medical Imaging: Health-information sharing, specifically of digital pathology, is the subject of this paper which discusses how sharing the rich images in pathology can stretch the capabilities of all otherwise well-practiced disciplines.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.Expert review of molecular diagnostics · 2026Review
- Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.Toxicological research · 2026Article
- Biological Data Resources and Machine Learning Frameworks for Hematology Research.Genomics, proteomics & bioinformatics · 2025Review
- Advancements in pathology: Digital transformation, precision medicine, and beyond.Journal of pathology informatics · 2025Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Artificial intelligence for dementia genetics and omics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials.NPJ precision oncology · 2022Article
- Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.The American journal of pathology · 2021Review
- GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.Laboratory investigation; a journal of technical methods and pathology · 2021Article
- Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of ten cancer types.Scientific data · 2020Article
- Robust Histopathology Image Analysis: to Label or to Synthesize?Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2019Article
- Review of quantitative multiscale imaging of breast cancer.Journal of medical imaging (Bellingham, Wash.) · 2018Review
- An Assessment of Imaging Informatics for Precision Medicine in Cancer.Yearbook of medical informatics · 2017Review
- Overcoming therapeutic resistance in glioblastoma: the way forward.The Journal of clinical investigation · 2017Review
- Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images.Laboratory investigation; a journal of technical methods and pathology · 2015Review
- Summary of 2(nd) Nordic symposium on digital pathology.Journal of pathology informatics · 2015Article
- Human Factors and Human-Computer Considerations in Teleradiology and Telepathology.Healthcare (Basel, Switzerland) · 2014Review
- 3DPro: Querying Complex Three-Dimensional Data with Progressive Compression and Refinement.Advances in database technology : proceedings. International Conference on Extending Database TechnologyArticle
- An Interactive Learning Framework for Scalable Classification of Pathology Images.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big DataArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Pathology is a medical subspecialty that practices the diagnosis of disease. Microscopic examination of tissue reveals information enabling the pathologist to render accurate diagnoses and to guide therapy. The basic process by which anatomic pathologists render diagnoses has remained relatively unchanged over the last century, yet advances in information technology now offer significant opportunities in image-based diagnostic and research applications. Pathology has lagged behind other healthcare practices such as radiology where digital adoption is widespread. As devices that generate whole slide images become more practical and affordable, practices will increasingly adopt this technology and eventually produce an explosion of data that will quickly eclipse the already vast quantities of radiology imaging data. These advances are accompanied by significant challenges for data management and storage, but they also introduce new opportunities to improve patient care by streamlining and standardizing diagnostic approaches and uncovering disease mechanisms. Computer-based image analysis is already available in commercial diagnostic systems, but further advances in image analysis algorithms are warranted in order to fully realize the benefits of digital pathology in medical discovery and patient care. In coming decades, pathology image analysis will extend beyond the streamlining of diagnostic workflows and minimizing interobserver variability and will begin to provide diagnostic assistance, identify therapeutic targets, and predict patient outcomes and therapeutic responses.
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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.