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

By A. Hekler, J. Utikal, Alexander H Enk, A. Hauschild, M. Weichenthal, Roman C. Maron, C. Berking, S. Haferkamp, J. Klode, Dirk Schadendorf and 89 more. European Journal of Cancer, 2019

308 citations recorded by the source corpus.

BACKGROUND In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. METHODS Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). FINDINGS Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% INTERPRETATION: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology

View Paper: https://www.semanticscholar.org/paper/78f2de830ed54d6a932a390a0ccd914033d4d2c2

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Citations in the corpus

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023, methodology): “had dermatologists and an ML model separately evaluate skin images to detect cancer and then combined those predictions using a decision tree-based ML algorithm called XGBoost to achieve performance superior to either method independently.(35) Image-based deep learning has also been used in other wa”
Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning (2022, unknown): “[25] 300 HAM10000 CNN with XGBoost Five”