ArticleFrontiers in oncology2020
Recognition of Cutaneous Melanoma on Digitized Histopathological Slides
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Image analysis of cutaneous melanoma histology: a systematic review and meta-analysis.Scientific reports · 2023Pooled it
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Early detection of metastatic risk in primary cutaneous melanoma using weakly supervised learning.Scientific reports · 2026Article
- A Multitask Deep Learning Approach for Tumor Segmentation and Morphological Quantification in Acral Melanoma Whole-Slide Images.Journal of imaging informatics in medicine · 2026Article
- Large-Scale Dermatopathology Dataset for Lesion Segmentation: Model Development and Analysis.Journal of Korean medical science · 2025Article
- EGFR Mutation Detection in Whole Slide Images of Non-Small Cell Lung Cancers Using a Two-Stage Deep Transfer Learning Approach.Cancer medicine · 2025Article
- Development of a Machine Learning Model Integrating Pathomics and Clinical Data to Predict Axillary Lymph Node Metastasis in Breast Cancer: A Two-Center Study.Cancer reports (Hoboken, N.J.) · 2025Article
- Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.Journal of imaging informatics in medicine · 2025Article
- Mini review on skin biopsy: traditional and modern techniques.Frontiers in medicine · 2025Review
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Deep learning model shows pathologist-level detection of sentinel node metastasis of melanoma and intra-nodal nevi on whole slide images.Frontiers in medicine · 2024Article
- Artificial intelligence and skin cancer.Frontiers in medicine · 2024Review
- Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.Cells · 2023Article
- Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases.EBioMedicine · 2023Article
- Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods.Current oncology reports · 2023Review
- Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning.Journal of pathology informatics · 2023Article
- Review
- Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma.Cancers · 2022Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing incidence of skin cancer combined with a shortage of dermatopathologists has increased the workload of pathology departments worldwide. In addition, the high intraobserver and interobserver variability in the assessment of melanocytic skin lesions can result in underestimated or overestimated diagnosis of melanoma. Thus, the development of new techniques for skin tumor diagnosis is essential to assist pathologists to standardize diagnoses and plan accurate patient treatment. Here, we describe the development of an artificial intelligence (AI) system that recognizes cutaneous melanoma from histopathological digitalized slides with clinically acceptable accuracy. Whole-slide digital images from 100 formalin-fixed paraffin-embedded primary cutaneous melanoma were used to train a convolutional neural network (CNN) based on a pretrained Inception-ResNet-v2 to accurately and automatically differentiate tumoral areas from healthy tissue. The CNN was trained by using 60 digital slides in which regions of interest (ROIs) of tumoral and healthy tissue were extracted by experienced dermatopathologists, while the other 40 slides were used as test datasets. A total of 1377 patches of healthy tissue and 2141 patches of melanoma were assessed in the training/validation set, while 791 patches of healthy tissue and 1122 patches of pathological tissue were evaluated in the test dataset. Considering the classification by expert dermatopathologists as reference, the trained deep net showed high accuracy (96.5%), sensitivity (95.7%), specificity (97.7%), F
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