Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm.

By S. Han, Myoung Shin Kim, Woohyung Lim, Gyeong-Hun Park, I. Park, S. Chang. Journal of Investigative Dermatology, 2018

618 citations recorded by the source corpus.

We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from the training portion of the Asan dataset, MED-NODE dataset, and atlas site images (19,398 images in total). The trained model was validated with the testing portion of the Asan, Hallym and Edinburgh datasets. With the Asan dataset, the area under the curve for the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 ± 0.01, 0.83 ± 0.01, 0.82 ± 0.02, and 0.96 ± 0.00, respectively. With the Edinburgh dataset, the area under the curve for the corresponding diseases was 0.90 ± 0.01, 0.91 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. With the Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% ± 6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was comparable to that of 16 dermatologists. To improve the performance of convolutional neural network, additional images with a broader range of ages and ethnicities should be collected.

Subfields: Artificial Intelligence, Epidemiology, Oncology

View Paper: https://www.semanticscholar.org/paper/561af930ceb87cc54677e16e7d4451b8ad646771

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Citations in the corpus

Shifting machine learning for healthcare from development to deployment and from models to data (2022, background): “…tone decrease in performance when evaluated on images of different skin tones 173 ; in this case, the drop in performance could be attributed to variations in disease presentation that are not captured when certain patient populations are not adequately represented in the training dataset 174 .”
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset (2021, background): “While these labels are not known to be confirmed by a biopsy, these images and their skin condition labels have been used and cited in dermatology and computer vision literature a number of times [23, 29, 9, 45, 6, 50, 53].”
Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review (2018, background): “Han et al [27] are particularly noteworthy for their scientific transparency since they have made their computer algorithm publicly available for external testing.”
Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study (2019, background): “Subsequently, Esteva and colleagues 13 and Han and colleagues 14 fine-tuned convolutional neural networks with large datasets of clinical images and observed dermatologist-level accuracy for general skin disease classification.”
Precision Medicine, AI, and the Future of Personalized Health Care (2020, methodology): “90 AI-based image analysis has become a part of a direct-to-consumer diagnostic tool for anemia as well.”