ArticleCancers2023
Deep Learning Approach to Classify Cutaneous Melanoma in a Whole Slide Image.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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
8 citing papers in PubMed, 16 citations in OpenAlex.
- Digital Pathology-Based Comparison of PyRadiomics and HistomicsTK for Nuclei Classification in Melanoma Whole Slide Images.International journal of biomedical imaging · 2026Article
- ViT-Stain: Vision transformer-driven virtual staining for skin histopathology via global contextual learning.PloS one · 2026Article
- Artificial Intelligence for Lentigo Maligna: Automated Margin Assessment via Sox-10-Based Melanocyte Density Mapping.Dermatopathology (Basel, Switzerland) · 2025Article
- Artificial intelligence-based triaging of cutaneous melanocytic lesions.npj biomedical innovations · 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
- Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images-Nevus and Melanoma.Cancers · 2024Article
- Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.PloS one · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Although the histopathological diagnosis of cutaneous melanocytic lesions is fairly accurate and reliable among experienced surgical pathologists, it is not perfect in every case (especially melanoma). Microscopic examination-clinicopathological correlation is the gold standard for the definitive diagnosis of melanoma. Pathologists may encounter diagnostic controversies when melanoma closely mimics Spitz's nevus or blue nevus, exhibits amelanotic histopathology, or is in situ. It would be beneficial if diagnosing cutaneous melanocytic lesions can be automated by using deep learning, particularly when assisting surgical pathologists with their workloads. In this preliminary study, we investigated the application of deep learning for classifying cutaneous melanoma in whole-slide images (WSIs). We trained models via weakly supervised learning using a dataset of 66 WSIs (33 melanomas and 33 non-melanomas). We evaluated the models on a test set of 90 WSIs (40 melanomas and 50 non-melanomas), achieving ROC-AUC at 0.821 for the WSI level and 0.936 for the tile level by the best model.
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What OpenQuestion holds
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.