ArticleScientific reports2017
Interactive phenotyping of large-scale histology imaging data with HistomicsML.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Plant Microtechnique with Resin: Towards Plant Histolomics.Plants (Basel, Switzerland) · 2026Article
- MobileDANet integrating transfer learning and dynamic attention for classifying multi target histopathology images with explainable AI.Scientific reports · 2025Article
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
- Review
- Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.Npj imaging · 2024Review
- Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology.Frontiers in oncology · 2024Review
- DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs.Journal of digital imaging · 2023Article
- Biologically Informed Clot Histomics Are Predictive of Acute Ischemic Stroke Etiology.Stroke (Hoboken, N.J.) · 2023Article
- General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology.Diagnostics (Basel, Switzerland) · 2022Article
- NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.GigaScience · 2022Article
- Molecular Pathology of Lung Cancer.Cold Spring Harbor perspectives in medicine · 2022Review
- Large-scale extraction of interpretable features provides new insights into kidney histopathology - A proof-of-concept study.Journal of pathology informatics · 2022Article
- Biological data annotation via a human-augmenting AI-based labeling system.NPJ digital medicine · 2021Article
- Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.Acta neuropathologica communications · 2021Article
- Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.Histopathology · 2021Review
- Development and operation of a digital platform for sharing pathology image data.BMC medical informatics and decision making · 2021Article
- A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.International journal of molecular sciences · 2021Review
- Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.Cancer research · 2021Article
- Computational analysis of morphological and molecular features in gastric cancer tissues.Cancer medicine · 2020Article
- Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.NPJ breast cancer · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe tissues, yielding measurements for hundreds of millions of histologic objects. A central challenge in utilizing this data is enabling investigators to train and evaluate classification rules for identifying objects related to processes like angiogenesis or immune response. In this paper we describe HistomicsML, an interactive machine-learning system for digital pathology imaging datasets. This framework uses active learning to direct user feedback, making classifier training efficient and scalable in datasets containing 10
Indexed as
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.