IPROC iproc Iproceedings 2369-6893 JMIR Publications Toronto, Canada v7i1e35437 27739472 10.2196/35437 Abstract Abstract Explainability of Convolutional Neural Networks for Dermatological Diagnosis Derrick Thomas Jalaboi Raluca MSc 1
Technical University of Denmark Anker Engelunds Vej 1 Bygning 101A, 2800 Kgs Kongens Lyngby Denmark 45 45 25 25 25 rjal@dtu.dk
https://orcid.org/0000-0001-6269-0527 Orbes Arteaga Mauricio MSc 2 https://orcid.org/0000-0002-4901-4230 Richter Jørgensen Dan PhD 2 https://orcid.org/0000-0003-3801-4523 Manole Ionela MD 2 https://orcid.org/0000-0002-3323-102X Bozdog Oana Ionescu MD 2 https://orcid.org/0000-0001-6421-3134 Chiriac Andrei MD 2 https://orcid.org/0000-0002-8514-7567 Winther Ole PhD 1 https://orcid.org/0000-0002-1966-3205 Galimzianova Alfiia PhD 2 https://orcid.org/0000-0002-2901-6423 Technical University of Denmark Kongens Lyngby Denmark Omhu Copenhagen Denmark Corresponding Author: Raluca Jalaboi rjal@dtu.dk Jan-Dec 2021 10 12 2021 7 1 e35437 3 12 2021 3 12 2021 ©Raluca Jalaboi, Mauricio Orbes Arteaga, Dan Richter Jørgensen, Ionela Manole, Oana Ionescu Bozdog, Andrei Chiriac, Ole Winther, Alfiia Galimzianova. Originally published in Iproceedings (https://www.iproc.org), 10.12.2021. 2021

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 Background

Convolutional neural networks (CNNs) are regarded as state-of-the-art artificial intelligence (AI) tools for dermatological diagnosis, and they have been shown to achieve expert-level performance when trained on a representative dataset. CNN explainability is a key factor to adopting such techniques in practice and can be achieved using attention maps of the network. However, evaluation of CNN explainability has been limited to visual assessment and remains qualitative, subjective, and time consuming.

 Objective

This study aimed to provide a framework for an objective quantitative assessment of the explainability of CNNs for dermatological diagnosis benchmarks.

 Methods

We sourced 566 images available under the Creative Commons license from two public datasets—DermNet NZ and SD-260, with reference diagnoses of acne, actinic keratosis, psoriasis, seborrheic dermatitis, viral warts, and vitiligo. Eight dermatologists with teledermatology expertise annotated each clinical image with a diagnosis, as well as diagnosis-supporting characteristics and their localization. A total of 16 supporting visual characteristics were selected, including basic terms such as macule, nodule, papule, patch, plaque, pustule, and scale, and additional terms such as closed comedo, cyst, dermatoglyphic disruption, leukotrichia, open comedo, scar, sun damage, telangiectasia, and thrombosed capillary. The resulting dataset consisted of 525 images with three rater annotations for each. Explainability of two fine-tuned CNN models, ResNet-50 and EfficientNet-B4, was analyzed with respect to the reference explanations provided by the dermatologists. Both models were pretrained on the ImageNet natural image recognition dataset and fine-tuned using 3214 images of the six target skin conditions obtained from an internal clinical dataset. CNN explanations were obtained as activation maps of the models through gradient-weighted class-activation maps. We computed the fuzzy sensitivity and specificity of each characteristic attention map with regard to both the fuzzy gold standard characteristic attention fusion masks and the fuzzy union of all characteristics.

 Results

On average, explainability of EfficientNet-B4 was higher than that of ResNet-50 in terms of sensitivity for 13 of 16 supporting characteristics, with mean values of 0.24 (SD 0.07) and 0.16 (SD 0.05), respectively. However, explainability was lower in terms of specificity, with mean values of 0.82 (SD 0.03) and 0.90 (SD 0.00) for EfficientNet-B4 and ResNet-50, respectively. All measures were within the range of corresponding interrater metrics.

 Conclusions

We objectively benchmarked the explainability power of dermatological diagnosis models through the use of expert-defined supporting characteristics for diagnosis.

 Acknowledgments

This work was supported in part by the Danish Innovation Fund under Grant 0153-00154A.

 Conflict of Interest

None declared.

 dermatology explainability convolutional neural networks

Explainability of ResNet-50 and EfficientNet-B4 models in terms of sensitivity between dermatologists-provided segmented supporting characteristics and model activation maps. All activation maps were computed based on the gold standard diagnosis using gradient-weighted class-activation maps. Interrater sensitivity is computed as the pairwise average for dermatologist-provided supporting characteristic segmentations.

Examples of explanations for images where both models correctly predicted the gold standard diagnosis. From left to right: the original image, the union of all characteristics selected by all dermatologists annotating the image, an EfficientNet-B4 gradient-weighted class-activation map (Grad-CAM) visualization, and a ResNet-50 Grad-CAM visualization. In all cases, the EfficientNet-B4 visualization was closer to the dermatologist map than the ResNet-50 visualization. ResNet-50 appears to be more specific, focusing on smaller, more noticeable lesions.