ArticleScientific reports2020
Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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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.
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Who cites it
37 citing papers in PubMed, 110 citations in OpenAlex.
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Foundation models in computational pathology: methods, applications and clinical implications.BMJ oncology · 2026Review
- Large-Scale Dermatopathology Dataset for Lesion Segmentation: Model Development and Analysis.Journal of Korean medical science · 2025Article
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
- Automated determination of tumor cell percentages in whole slide images: A nuclear classification study for molecular pathology tests.Journal of pathology informatics · 2025Article
- Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.BMC medical imaging · 2025Article
- Assessing the performance of an artificial intelligence based chatbot in the differential diagnosis of oral mucosal lesions: clinical validation study.Clinical oral investigations · 2025Article
- Mini review on skin biopsy: traditional and modern techniques.Frontiers in medicine · 2025Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review.Journal of imaging · 2024Review
- Article
- Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images-Nevus and Melanoma.Cancers · 2024Article
- Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images - Nevus & Melanoma.ArXiv · 2024Article
- Synergies and Challenges in the Preclinical and Clinical Implementation of Pathology Artificial Intelligence Applications.Mayo Clinic proceedings. Digital health · 2023Review
- High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning.Journal of pathology informatics · 2023Article
- Applications for Machine Learning in Mohs Micrographic Surgery: Increased Efficiency and Accuracy.Kansas journal of medicine · 2023Article
- Advances in melanoma: epidemiology, diagnosis, and prognosis.Frontiers in medicine · 2023Review
- Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma.Cancers · 2022Review
- Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Review
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system's use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications.
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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.