ArticleScientific reports2021
PathoNet introduced as a deep neural network backend for evaluation of Ki-67 and tumor-infiltrating lymphocytes in breast cancer.
Article in Scientific reports, 2021. 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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27 citing papers in PubMed, 59 citations in OpenAlex.
- ShadoNet: a cell detection and classification framework for Ki-67 pathology images.Bioinformatics (Oxford, England) · 2026Article
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Integrated local and global Ki67 profiling for prognostic stratification and therapeutic guidance in high-grade neuroendocrine neoplasms: a multicenter validation study.International journal of surgery (London, England) · 2025Article
- Multi scale deep learning quantifies Ki67 index in breast cancer histopathology images.Scientific reports · 2025Article
- Automated quantification of Ki-67 expression in breast cancer from H&E-stained slides using a transformer-based regression model.Breast cancer research : BCR · 2025Article
- Artificial Intelligence-Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer.Cancer medicine · 2025Review
- Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions.Bioelectronic medicine · 2025Review
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep learning based analysis of G3BP1 protein expression to predict the prognosis of nasopharyngeal carcinoma.PloS one · 2025Article
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- Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.Scientific reports · 2024Article
- Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence.Open life sciences · 2024Article
- Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model.Frontiers in immunology · 2024Article
- AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images.Cancers · 2023Article
- Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN.Scientific reports · 2023Article
- The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?-A Case Report and Review of the Literature.Medicina (Kaunas, Lithuania) · 2023Review
- SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data.Frontiers in genetics · 2023Article
- A Review of Watershed Implementations for Segmentation of Volumetric Images.Journal of imaging · 2022Review
- Deep Learning-Inferred Multiplex ImmunoFluorescence for Immunohistochemical Image Quantification.Nature machine intelligence · 2022Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The nuclear protein Ki-67 and Tumor infiltrating lymphocytes (TILs) have been introduced as prognostic factors in predicting both tumor progression and probable response to chemotherapy. The value of Ki-67 index and TILs in approach to heterogeneous tumors such as Breast cancer (BC) that is the most common cancer in women worldwide, has been highlighted in literature. Considering that estimation of both factors are dependent on professional pathologists' observation and inter-individual variations may also exist, automated methods using machine learning, specifically approaches based on deep learning, have attracted attention. Yet, deep learning methods need considerable annotated data. In the absence of publicly available benchmarks for BC Ki-67 cell detection and further annotated classification of cells, In this study we propose SHIDC-BC-Ki-67 as a dataset for the aforementioned purpose. We also introduce a novel pipeline and backend, for estimation of Ki-67 expression and simultaneous determination of intratumoral TILs score in breast cancer cells. Further, we show that despite the challenges that our proposed model has encountered, our proposed backend, PathoNet, outperforms the state of the art methods proposed to date with regard to harmonic mean measure acquired. Dataset is publicly available in http://shiraz-hidc.com and all experiment codes are published in https://github.com/SHIDCenter/PathoNet .
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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.