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Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

https://doi.org/10.1016/j.ejca.2019.06.012

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

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ImageNet Large Scale Visual Recognition Challenge

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Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark

inferredcites →https://doi.org/10.1016/j.ejca.2019.02.005openalex-snapshot

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

inferredcites →https://doi.org/10.1016/j.ejca.2019.04.001openalex-snapshot

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

inferredcites →https://doi.org/10.1016/j.ejca.2019.04.021openalex-snapshot

Pathologist-level classification of histopathological melanoma images with deep neural networks

inferredcites →https://doi.org/10.1016/j.ejca.2019.05.023openalex-snapshot

Deep neural networks are superior to dermatologists in melanoma image classification

inferredcites →https://doi.org/10.1016/j.ejca.2019.06.013openalex-snapshot

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

inferredcites →https://doi.org/10.1016/j.ejca.2019.07.009openalex-snapshot

Prediction of melanoma evolution in melanocytic nevi via artificial intelligence: A call for prospective data

inferredcites →https://doi.org/10.1016/j.ejca.2019.07.019openalex-snapshot

Superior skin cancer classification by the combination of human and artificial intelligence

inferredcites →https://doi.org/10.1016/j.jaad.2017.08.016openalex-snapshot

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

inferredcites →https://doi.org/10.1016/j.media.2016.06.037openalex-snapshot

Image analysis and machine learning in digital pathology: Challenges and opportunities

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Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

inferredcites →https://doi.org/10.1038/nature21056openalex-snapshot

Dermatologist-level classification of skin cancer with deep neural networks

inferredcites →https://doi.org/10.1038/ncomms12474openalex-snapshot

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

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Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

inferredcites →https://doi.org/10.1109/cvpr.1993openalex-snapshot

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

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Interobserver variability on the histopathologic diagnosis of cutaneous melanoma and other pigmented skin lesions.

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Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

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SGDR: Stochastic Gradient Descent with Warm Restarts

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Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

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Re: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images

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Multi-resolution vision transformer model for skin cancer subtype classification using histopathology slides

inferred← citeshttps://doi.org/10.1108/ijius-02-2021-0010openalex-snapshot

Multiclass skin lesion classification using image augmentation technique and transfer learning models

inferred← citeshttps://doi.org/10.1109/access.2021.3132958openalex-snapshot

Scale-Aware Transformers for Diagnosing Melanocytic Lesions

inferred← citeshttps://doi.org/10.1109/access.2022.3199613openalex-snapshot

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

inferred← citeshttps://doi.org/10.1109/access.2023.3339635openalex-snapshot

Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions

inferred← citeshttps://doi.org/10.1109/access.2025.3594706openalex-snapshot

Optimization of Convolutional Neural Networks With Multi-Objective Function Metaheuristics for Melanoma Detection

inferred← citeshttps://doi.org/10.1109/aide69088.2026.11544172openalex-snapshot

Skin Cancer Detection and Classification Using Deep Learning Techniques

inferred← citeshttps://doi.org/10.1109/gseact68539.2026.11620218openalex-snapshot

CNN-Based Hair Fall Stage and Scalp Disease Classification

inferred← citeshttps://doi.org/10.1109/iccis54243.2021.9676187openalex-snapshot

Comparative Study of Deep Learning Models For Automatic Detection of Metastases in H&E Stained Images

inferred← citeshttps://doi.org/10.1109/icct-pacific63901.2025.11012773openalex-snapshot

Deep Learning-Based Skin Lesion Classification with Ensemble Stacking and Data Augmentation

inferred← citeshttps://doi.org/10.1109/iccubea58933.2023.10392174openalex-snapshot

Advancements in Melanoma Skin Cancer Detection Using Deep Learning: A Comprehensive Review

inferred← citeshttps://doi.org/10.1109/icict57646.2023.10134277openalex-snapshot

Exploring the Superiority of CapsNet over CNN For Early Detection of Lung Cancer A Comparative Analysis

inferred← citeshttps://doi.org/10.1109/icimia67127.2025.11200886openalex-snapshot

AI-Driven Dermatological Cancer Detection: A Vision Transformer Based Novel Approach

inferred← citeshttps://doi.org/10.1109/icrito48877.2020.9197820openalex-snapshot

Study of Melanoma Detection and Classification Techniques

inferred← citeshttps://doi.org/10.1109/ijcnn52387.2021.9533659openalex-snapshot

Learn and Visually Explain Deep Fair Models: an Application to Face Recognition

inferred← citeshttps://doi.org/10.1109/ijcnn55064.2022.9892372openalex-snapshot

The Importance of Multiple Temporal Scales in Motion Recognition: when Shallow Model can Support Deep Multi Scale Models

inferred← citeshttps://doi.org/10.1109/ipas50080.2020.9334956openalex-snapshot

Convolutional Neural Network Based Desktop Applications to Classify Dermatological Diseases

inferred← citeshttps://doi.org/10.1109/isbi56570.2024.10635208openalex-snapshot

In Silico Optimization of Tissue Microarray Design for Machine Learning Analysis

inferred← citeshttps://doi.org/10.1109/iv51561.2020.00130openalex-snapshot

Deep Learning in Skin Lesion Analysis Towards Cancer Detection

inferred← citeshttps://doi.org/10.1109/jbhi.2021.3081185openalex-snapshot

Skin Melanoma Detection in Microscopic Images Using HMM-Based Asymmetric Analysis and Expectation Maximization

inferred← citeshttps://doi.org/10.1109/ntci60157.2023.10403690openalex-snapshot

Glaucoma Fundus Image Recognition Based on Optimal ROI Proportion and VIT-B/16

inferred← citeshttps://doi.org/10.1109/stdh66836.2025.11227486openalex-snapshot

Extraction of Informative Features for Optical Biopsy Data Analysis

inferred← citeshttps://doi.org/10.1109/tfuzz.2020.2995968openalex-snapshot

CoMHisP: A Novel Feature Extractor for Histopathological Image Classification Based on Fuzzy SVM With Within-Class Relative Density

inferred← citeshttps://doi.org/10.1109/tim.2023.3251409openalex-snapshot

Augmentation-Assisted Robust Fringe Detection on Unseen Experimental Signals Applied to Optical Feedback Interferometry Using a Deep Network

inferred← citeshttps://doi.org/10.1109/tmi.2022.3166129openalex-snapshot

Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging

inferred← citeshttps://doi.org/10.1109/tuffc.2022.3221682openalex-snapshot

Current Advances in Computational Lung Ultrasound Imaging: A Review

inferred← citeshttps://doi.org/10.1109/urtc68753.2025.11533057openalex-snapshot

Histopathology-Based Deep Learning for Melanoma PDX Drug-Response Tracking

inferred← citeshttps://doi.org/10.1111/1346-8138.15683openalex-snapshot

Development of a light‐weight deep learning model for cloud applications and remote diagnosis of skin cancers

inferred← citeshttps://doi.org/10.1111/2041-210x.13703openalex-snapshot

Machine learning is a powerful tool to study the effect of cancer on species and ecosystems

inferred← citeshttps://doi.org/10.1111/bju.15386openalex-snapshot

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer

inferred← citeshttps://doi.org/10.1111/ddg.14233openalex-snapshot

Overdiagnosis of melanoma – causes, consequences and solutions

inferred← citeshttps://doi.org/10.1111/ddg.14233_gopenalex-snapshot

Überdiagnose von Melanomen – Ursachen, Konsequenzen und Lösungsansätze

inferred← citeshttps://doi.org/10.1111/ddg.15180openalex-snapshot

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma

inferred← citeshttps://doi.org/10.1111/ddg.15180_gopenalex-snapshot

Einsatz künstlicher Intelligenz mittels Deep Learning in der dermatopathologischen Routinediagnostik des Basalzellkarzinoms

inferred← citeshttps://doi.org/10.1111/ddg.15322openalex-snapshot

Basic principles of artificial intelligence in dermatology explained using melanoma

inferred← citeshttps://doi.org/10.1111/ddg.15322_gopenalex-snapshot

Grundprinzipien der künstlichen Intelligenz in der Dermatologie erklärt am Beispiel des Melanoms

inferred← citeshttps://doi.org/10.1111/den.13875openalex-snapshot

Artificial intelligence for the management of pancreatic diseases

inferred← citeshttps://doi.org/10.1111/exd.14306openalex-snapshot

Whole‐slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma

inferred← citeshttps://doi.org/10.1111/his.15271openalex-snapshot

Prediction of molecular subclasses of uveal melanoma by deep learning using routine haematoxylin–eosin‐stained tissue slides

inferred← citeshttps://doi.org/10.1111/jdv.20319openalex-snapshot

Skinformatics: Navigating the big data landscape of dermatology

inferred← citeshttps://doi.org/10.1111/jdv.70000openalex-snapshot

A deep learning–clinical nomogram hybrid for predicting sentinel lymph node metastasis in melanoma

inferred← citeshttps://doi.org/10.1111/jocd.15323openalex-snapshot

Artificial intelligence in melanoma: A systematic review

inferred← citeshttps://doi.org/10.1111/joim.13030openalex-snapshot

Artificial intelligence as the next step towards precision pathology

inferred← citeshttps://doi.org/10.1136/gutjnl-2020-322880openalex-snapshot

Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers

inferred← citeshttps://doi.org/10.1136/jitc-2024-011346openalex-snapshot

Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction

inferred← citeshttps://doi.org/10.1142/s0217984921506193openalex-snapshot

Classification of melanoma images using 2D multifractal detrended cross-correlation analysis

inferred← citeshttps://doi.org/10.1145/3531146.3533193openalex-snapshot

Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging

inferred← citeshttps://doi.org/10.1145/3549206.3549281openalex-snapshot

An approach for classifying benign and malignant skin lesions using Optimized Deep Learning and SVM

inferred← citeshttps://doi.org/10.1145/3665348.3665418openalex-snapshot

Recognition of Skin Cancer from Dermoscopic Images via DNN

inferred← citeshttps://doi.org/10.1155/2022/1734327openalex-snapshot

Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review

inferred← citeshttps://doi.org/10.1158/1055-9965.epi-20-0075openalex-snapshot

Radiomics Improves Cancer Screening and Early Detection

inferred← citeshttps://doi.org/10.11603/mie.1996-1960.2022.4.13411openalex-snapshot

ЦИФРОВА ПАТОЛОГІЯ ПРИ МЕЛАНОМІ: ДОСЯГНЕННЯ, БАР’ЄРИ ТА ПЕРСПЕКТИВИ

inferred← citeshttps://doi.org/10.1177/15330338241257479openalex-snapshot

Detection of Muscularis propria Invasion in Urothelial Carcinoma Using Artificial Intelligence

inferred← citeshttps://doi.org/10.1186/s12880-022-00871-wopenalex-snapshot

Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images

inferred← citeshttps://doi.org/10.1186/s12967-026-08829-0openalex-snapshot

Agentic systems in computational pathology: architectures, evidence, and translational challenges

inferred← citeshttps://doi.org/10.1186/s40478-020-00927-4openalex-snapshot

Validation of machine learning models to detect amyloid pathologies across institutions

inferred← citeshttps://doi.org/10.1186/s40537-022-00634-yopenalex-snapshot

Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system

inferred← citeshttps://doi.org/10.1186/s42047-022-00113-xopenalex-snapshot

Computational pathology, new horizons and challenges for anatomical pathology

inferred← citeshttps://doi.org/10.1200/cci.20.00045openalex-snapshot

Joint Imaging Platform for Federated Clinical Data Analytics

inferred← citeshttps://doi.org/10.12677/acrpvm.2020.94008openalex-snapshot

Pathological Diagnosis and Analysis of Four Cases of Melanoma in Yunan Black Pigs

inferred← citeshttps://doi.org/10.12775/jehs.2022.12.09.095openalex-snapshot

New Opportunities and Challenges for Health Professionals in the era of Artificial Intelligence – Review

inferred← citeshttps://doi.org/10.1371/journal.pcbi.1010718openalex-snapshot

Eleven quick tips for data cleaning and feature engineering

inferred← citeshttps://doi.org/10.1371/journal.pdig.0000091openalex-snapshot

Multicenter automatic detection of invasive carcinoma on breast whole slide images

inferred← citeshttps://doi.org/10.1371/journal.pone.0278542openalex-snapshot

Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence

inferred← citeshttps://doi.org/10.1515/9783111670744-012openalex-snapshot

265Chapter 12 AI solutions for skin cancer diagnosis and treatment

inferred← citeshttps://doi.org/10.17485/ijst/v16i45.2658openalex-snapshot

A Comparative Study of Machine Learning Models for Early Detection of Skin Cancer Using Convolutional Neural Networks

inferred← citeshttps://doi.org/10.17513/spno.33506openalex-snapshot

POSSIBILITIES OF APPLYING ARTIFICIAL INTELLIGENCE IN PATHOMORPHOLOGICAL DIAGNOSIS OF SKIN DISEASES

inferred← citeshttps://doi.org/10.17513/spno.33556openalex-snapshot

POSSIBILITIES OF ARTIFICIAL INTELLIGENCE IN ONCOUROLOGY

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