ArticlePloS one2018
Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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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.
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Who cites it
25 citing papers in PubMed, 53 citations in OpenAlex.
- Article
- Ki-67 as a Prognostic Marker in Squamous Cell Carcinomas of the Vulva: A Systematic Review.Journal of clinical medicine · 2025Review
- Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and barriers to clinical implementation.Frontiers in oncology · 2025Review
- Automated AI-based grading of neuroendocrine tumors using Ki-67 proliferation index: comparative evaluation and performance analysis.Medical & biological engineering & computing · 2024Article
- Epigenetic Regulation of the Expression of T Cell Stimulatory and Inhibitory Factors by Histone H3 Lysine Modification Enzymes and Its Prognostic Roles in Glioblastoma.Journal of Korean medical science · 2023Article
- Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions.Diagnostics (Basel, Switzerland) · 2023Article
- Convolutional neural network for biomarker discovery for triple negative breast cancer with RNA sequencing data.Heliyon · 2023Article
- Preparing pathological data to develop an artificial intelligence model in the nonclinical study.Scientific reports · 2023Article
- Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.Frontiers in oncology · 2023Review
- Hypo-trimethylation of Histone H3 Lysine 4 and Hyper-tri/dimethylation of Histone H3 Lysine 27 as Epigenetic Markers of Poor Prognosis in Patients with Primary Central Nervous System Lymphoma.Cancer research and treatment · 2022Article
- Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery.Cancer biomarkers : section A of Disease markers · 2022Review
- Deep learning for the standardized classification of Ki-67 in vulva carcinoma: A feasibility study.Heliyon · 2021Article
- Article
- Astrocyte DISC1 contributes to cognitive function in a brain region-dependent manner.Human molecular genetics · 2020Article
- Round Robin Evaluation of MET Protein Expression in Lung Adenocarcinomas Improves Interobserver Concordance.Applied immunohistochemistry & molecular morphology : AIMM · 2020Article
- Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2020Article
- AI in Medical Imaging Informatics: Current Challenges and Future Directions.IEEE journal of biomedical and health informatics · 2020Article
- Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges.Clinical chemistry and laboratory medicine · 2020Review
- Progress of Artificial Intelligence in Gynecological Malignant Tumors.Cancer management and research · 2020Review
- Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
This paper addresses the problem of quantifying biomarkers in multi-stained tissues based on the color and spatial information of microscopy images of the tissue. A deep learning-based method that can automatically localize and quantify the regions expressing biomarker(s) in any selected area on a whole slide image is proposed. The deep learning network, which we refer to as Whole Image (WI)-Net, is a fully convolutional network whose input is the true RGB color image of a tissue and output is a map showing the locations of each biomarker. The WI-Net relies on a different network, Nuclei (N)-Net, which is a convolutional neural network that classifies each nucleus separately according to the biomarker(s) it expresses. In this study, images of immunohistochemistry (IHC)-stained slides were collected and used. Images of nuclei (4679 RGB images) were manually labeled based on the expressing biomarkers in each nucleus (as p16 positive, Ki-67 positive, p16 and Ki-67 positive, p16 and Ki-67 negative). The labeled nuclei images were used to train the N-Net (obtaining an accuracy of 92% in a test set). The trained N-Net was then extended to WI-Net that generated a map of all biomarkers in any selected sub-image of the whole slide image acquired by the scanner (instead of classifying every nucleus image). The results of our method compare well with the manual labeling by humans (average F-score of 0.96). In addition, we carried a layer-based immunohistochemical analysis of cervical epithelium, and showed that our method can be used by pathologists to differentiate between different grades of cervical intraepithelial neoplasia by quantitatively assessing the percentage of proliferating cells in the different layers of HPV positive lesions.
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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.