ArticleJournal of biomedical optics2020
Deep learning-level melanoma detection by interpretable machine learning and imaging biomarker cues.
Article in Journal of biomedical optics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Which explainable AI methods in medical imaging are clinically impactful? A systematic literature review addressing the clinician's perspective.Frontiers in artificial intelligence · 2026Pooled it
- Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.Mediators of inflammation · 2022Pooled it
- Enhancing Diagnostic Precision in Primary Care: A Multireader Multicase (MRMC) Study of an AI-Powered Handheld Elastic Scattering Spectroscopy Device for Informed Referral Decisions in Melanoma Evaluation.The Journal of clinical and aesthetic dermatology · 2025Article
- Global, regional, national prevalence, mortality, and disability-adjusted life-years of cutaneous squamous cell carcinoma and trend analysis from 1990 to 2021 and prediction to 2045.Frontiers in oncology · 2025Article
- Article
- Review
- Machine learning to predict overall short-term mortality in cutaneous melanoma.Discover oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 9 institutions in 5 countries.
Funding
Abstract
significanceMelanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics.
aimWe aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration. APPROACH: Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves.
resultsOur interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs.
conclusionsFrom a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.
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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.