ArticleCancers2020
piNET-An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Multi scale deep learning quantifies Ki67 index in breast cancer histopathology images.Scientific reports · 2025Article
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Article
- Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis.Medical & biological engineering & computing · 2024Article
- AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer.Scientific reports · 2024Article
- AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images.Cancers · 2023Article
- Unsupervised domain adaptive tumor region recognition for Ki67 automated assisted quantification.International journal of computer assisted radiology and surgery · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
In this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and whole slide images (WSI), representing a diverse multi-centre dataset for evaluating Ki67 quantification. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefficient R = 0.927. The success of the system can be attributed to several innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images-and it was posed as a detection problem to mimic pathologists' workflow which improves accuracy and efficiency. Secondly, the system is trained purely on tumor cells, which reduces false positives from non-tumor cells without needing the usual pre-requisite tumor segmentation step for Ki67 quantification. Thirdly, the concept of learning background regions through weak supervision is introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain "significant" activity.
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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.