ArticleBiomolecules & therapeutics2022
Classification of Mouse Lung Metastatic Tumor with Deep Learning.
Article in Biomolecules & therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- Navigating the New Therapeutic Landscape: Innovative Strategies for Overcoming Resistance and Degeneration.Biomolecules & therapeutics · 2026Article
- MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models.Pigment cell & melanoma research · 2025Article
- The state of the art for artificial intelligence in lung digital pathology.The Journal of pathology · 2022Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.
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Registered trials
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