ArticleCancer research2017
An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management.
Article in Cancer research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 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
31 citing papers in PubMed.
- Towards Accurate Breslow Measurements: Mitigating Issues in Histopathological Imaging.Entropy (Basel, Switzerland) · 2026Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- An update on applications of digital pathology: primary diagnosis; telepathology, education and research.Diagnostic pathology · 2025Review
- Digital pathology and multimodal learning on oncology data.BJR artificial intelligence · 2024Review
- Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies.Scientific reports · 2022Article
- General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology.Diagnostics (Basel, Switzerland) · 2022Article
- Explainable Biomarkers for Automated Glomerular and Patient-Level Disease Classification.Kidney360 · 2022Article
- Automatic Recognition of Ragged Red Fibers in Muscle Biopsy from Patients with Mitochondrial Disorders.Healthcare (Basel, Switzerland) · 2022Article
- Cell segmentation for immunofluorescence multiplexed images using two-stage domain adaptation and weakly labeled data for pre-training.Scientific reports · 2022Article
- MiNuGAN: Dual Segmentation of Mitoses and Nuclei Using Conditional GANs on Multi-center Breast H&E Images.Journal of pathology informatics · 2022Article
- Software tools and platforms in Digital Pathology: a review for clinicians and computer scientists.Journal of pathology informatics · 2022Review
- Machine learning in renal pathology.Frontiers in nephrology · 2022Article
- Quick Annotator: an open-source digital pathology based rapid image annotation tool.The journal of pathology. Clinical research · 2021Article
- SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Harnessing non-destructive 3D pathology.Nature biomedical engineering · 2021Review
- Machine learning techniques for mitoses classification.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2021Article
- piNET-An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images.Cancers · 2020Article
- AI in Medical Imaging Informatics: Current Challenges and Future Directions.IEEE journal of biomedical and health informatics · 2020Article
- MLCD: A Unified Software Package for Cancer Diagnosis.JCO clinical cancer informatics · 2020Article
- Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools.Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Pathology Image Informatics Platform (PIIP) is an NCI/NIH sponsored project intended for managing, annotating, sharing, and quantitatively analyzing digital pathology imaging data. It expands on an existing, freely available pathology image viewer, Sedeen. The goal of this project is to develop and embed some commonly used image analysis applications into the Sedeen viewer to create a freely available resource for the digital pathology and cancer research communities. Thus far, new plugins have been developed and incorporated into the platform for out of focus detection, region of interest transformation, and IHC slide analysis. Our biomarker quantification and nuclear segmentation algorithms, written in MATLAB, have also been integrated into the viewer. This article describes the viewing software and the mechanism to extend functionality by plugins, brief descriptions of which are provided as examples, to guide users who want to use this platform. PIIP project materials, including a video describing its usage and applications, and links for the Sedeen Viewer, plug-ins, and user manuals are freely available through the project web page: http://pathiip.org
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.