Abstract：
Convolutional neural networks (CNNs) have demonstrated expert-level performance in cutaneous tumour classification using clinical images, but most previous studies have focused on dermatologist-versus-CNN comparisons rather than their combination. The objective of our study was to evaluate the potential impact of CNN assistance on dermatologists for clinical image interpretation.
A multi-class CNN was trained and validated using a dataset of 25,773 clinical images comprising 10 categories of cutaneous tumours. The CNN\'s performance was tested on an independent dataset of 2107 images. A total of 400 images (40 per category) were randomly selected from the test dataset. A fully crossed, self-control, multi-reader multi-case (MRMC) study was conducted to compare the performance of 18 board-certified dermatologists (experience: 13/18 ≤ 10 years; 5/18＞10 years) in interpreting the 400 clinical images with or without CNN assistance.
The CNN achieved an overall accuracy of 78.45% and kappa of 0.73 in the classification of 10 types of cutaneous tumours on 2107 images. CNN-assisted dermatologists achieved a higher accuracy (76.60% vs. 62.78%, P < 0.001) and kappa (0.74 vs. 0.59, P < 0.001) than unassisted dermatologists in interpreting the 400 clinical images. Dermatologists with less experience benefited more from CNN assistance. At the binary classification level (malignant or benign), the sensitivity (89.56% vs. 83.21%, P < 0.001) and specificity (87.90% vs. 80.92%, P < 0.001) of dermatologists with CNN assistance were also significantly improved than those without.
CNN assistance improved dermatologist accuracy in interpreting cutaneous tumours and could further boost the acceptance of this new technique.
A multi-class CNN was trained and validated using a dataset of 25,773 clinical images comprising 10 categories of cutaneous tumours. The CNN\'s performance was tested on an independent dataset of 2107 images. A total of 400 images (40 per category) were randomly selected from the test dataset. A fully crossed, self-control, multi-reader multi-case (MRMC) study was conducted to compare the performance of 18 board-certified dermatologists (experience: 13/18 ≤ 10 years; 5/18＞10 years) in interpreting the 400 clinical images with or without CNN assistance.
The CNN achieved an overall accuracy of 78.45% and kappa of 0.73 in the classification of 10 types of cutaneous tumours on 2107 images. CNN-assisted dermatologists achieved a higher accuracy (76.60% vs. 62.78%, P < 0.001) and kappa (0.74 vs. 0.59, P < 0.001) than unassisted dermatologists in interpreting the 400 clinical images. Dermatologists with less experience benefited more from CNN assistance. At the binary classification level (malignant or benign), the sensitivity (89.56% vs. 83.21%, P < 0.001) and specificity (87.90% vs. 80.92%, P < 0.001) of dermatologists with CNN assistance were also significantly improved than those without.
CNN assistance improved dermatologist accuracy in interpreting cutaneous tumours and could further boost the acceptance of this new technique.
摘要：
卷积神经网络（CNN）已经证明了使用临床图像进行皮肤肿瘤分类的专家级性能，但是以前的大多数研究都集中在皮肤科医生与CNN的比较上，而不是它们的组合。我们研究的目的是评估CNN辅助对皮肤科医生临床图像解释的潜在影响。
使用包含10类皮肤肿瘤的25,773幅临床图像的数据集对多类CNN进行了训练和验证。CNN的性能在2107张图像的独立数据集上进行了测试。从测试数据集中随机选择总共400张图像(每个类别40张)。一个完全交叉，自我控制,我们进行了多读者多病例(MRMC)研究,比较了18名获得委员会认证的皮肤科医生(经验:13/18≤10年;5/18＞10年)在有或没有CNN辅助的情况下解释400张临床图像的表现.
CNN在2107张图像上对10种类型的皮肤肿瘤进行分类时，总体准确率为78.45%，κ为0.73。CNN辅助的皮肤科医生获得了更高的准确性（76.60％vs.62.78%,P<0.001）和κ（0.74vs.0.59，P<0.001）比无协助的皮肤科医生解释400张临床图像。经验较少的皮肤科医生从CNN的帮助中受益更多。在二元分类级别（恶性或良性），灵敏度(89.56%与83.21%,P<0.001）和特异性（87.90%vs.80.92%,有CNN辅助的皮肤科医生的P<0.001）也比没有的皮肤科医生明显改善。
CNN的帮助提高了皮肤科医生解释皮肤肿瘤的准确性，并可以进一步提高对这项新技术的接受度。
使用包含10类皮肤肿瘤的25,773幅临床图像的数据集对多类CNN进行了训练和验证。CNN的性能在2107张图像的独立数据集上进行了测试。从测试数据集中随机选择总共400张图像(每个类别40张)。一个完全交叉，自我控制,我们进行了多读者多病例(MRMC)研究,比较了18名获得委员会认证的皮肤科医生(经验:13/18≤10年;5/18＞10年)在有或没有CNN辅助的情况下解释400张临床图像的表现.
CNN在2107张图像上对10种类型的皮肤肿瘤进行分类时，总体准确率为78.45%，κ为0.73。CNN辅助的皮肤科医生获得了更高的准确性（76.60％vs.62.78%,P<0.001）和κ（0.74vs.0.59，P<0.001）比无协助的皮肤科医生解释400张临床图像。经验较少的皮肤科医生从CNN的帮助中受益更多。在二元分类级别（恶性或良性），灵敏度(89.56%与83.21%,P<0.001）和特异性（87.90%vs.80.92%,有CNN辅助的皮肤科医生的P<0.001）也比没有的皮肤科医生明显改善。
CNN的帮助提高了皮肤科医生解释皮肤肿瘤的准确性，并可以进一步提高对这项新技术的接受度。
