MULTI-LABEL PROTOTYPE BASED INTERPRETABLE MACHINE LEARNING FOR MELANOMA DETECTION
DOI:
https://doi.org/10.29284/ijasis.8.1.2022.40-53Keywords:
Skin lesion, dermatology, deep learning, interpretable ML, prototype-based, melanoma, seven-point checklistAbstract
Skin cancer is the most common of all cancers that exist in the world and melanoma is the deadliest among all the skin cancers. It is found that melanoma roughly kills a person every hour somewhere in the world. Considering the severity of the disease, significant effort goes into minimizing delays in the process of diagnosing melanoma. There are several approaches based on Machine Learning (ML) that can assist dermatologists in melanoma detection. However, many experts hesitate to trust ML systems due to their black-box nature, despite the accuracy of their performance. This highlights the need for applications that facilitate not only accurate classifications but also the ability to justify such decisions. In this work, we propose a prototype-based interpretable melanoma detector that uses the Seven Point Checklist, a well-known criterion used for the detection of melanoma. Prototypes provide the justification behind the decisions suggested by the ML model in a way of showing similar cases that are already known. In addition to identifying the dermoscopic features listed in the seven-point checklist, our work aims to provide reasoning that is similar to the ones used by the dermatologists in clinical practice for each decision made by the model. F1-Score has been used as the main performance metric in evaluating the model performance and that of the best performing class was 0.87. Furthermore, we show comparisons of our approach with Local Interpretable Model-Agnostic Explanations (LIME), a popular approach for interpretability for deep learning models.
References
Dermoscopedia, “Seven point checklist,” 2017.
X. Sun, J. Yang, M. Sun and K. Wang, “A benchmark for automatic visual classification of clinical skin disease images,” European Conference on Computer Vision, 2016, pp. 206–222.
A. G. Pacheco, G. R. Lima, A. S. Salomao, B. Krohling, I. P. Biral, G. G. de Angelo, F. C. Alves Jr, J. G. Esgario, A. C. Simora, P. B. Castro and F. B. Rodrigues, “PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones,” Data in brief, vol. 32, 2020, pp. 106221.
S. S. Han, M. S. Kim, W. Lim, G. H. Park, I. Park and S. E. Chang, “Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm,” Journal of Investigative Dermatology, vol. 138, no. 7, 2018, pp. 1529–1538.
B. Xie, X. He, S. Zhao, Y. Li, J. Su, X. Zhao, Y. Kuang, Y. Wang and X. Chen, “XiangyaDerm: a clinical image dataset of Asian race for skin disease aided diagnosis,” Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, 2019, pp. 22–31.
T. Mendonca, P. M. Ferreira, J. S. Marques, A. R. Marcal and J. Rozeira, “PH2- A dermoscopic image database for research and benchmarking,” 35th annual international conference of the IEEE engineering in medicine and biology society, 2013, pp. 5437–5440.
I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman and N. Petkov, “MED-NODE: A computer-assisted melanoma diagnosis system using non- dermoscopic images,” Expert systems with applications, vol. 42, no. 19, 2015, pp. 6578–6585.
P. Tschandl, C. Rosendahl and H. Kittler, “The HAM10000 dataset, a large collection of multi-sourcedermatoscopic images of common pigmented skin lesions,” Scientific data, vol. 5, no. 1, 2018, pp. 1–9.
J. Kawahara, S. Daneshvar, G. Argenziano and G. Hamarneh, “Seven-point checklist and skin lesion classification using multitask multimodal neural nets,” IEEE journal of biomedical and health informatics, vol. 23, no. 2, 2018, pp. 538–546.
L. Singh, R. R. Janghel and S. P. Sahu, “A deep learning-based transfer learning framework for the early detection and classification of dermoscopic images of melanoma,” Biomedical and Pharmacology Journal, vol. 14, no. 3, 2021, pp. 1231–1247.
A. A. Adegun and S. Viriri, “Deep learning-based system for automatic melanoma detection,” IEEE Access, vol. 8, 2019, pp. 7160–7172.
J. A. A. Salido and C. Ruiz, “Using deep learning to detect melanoma in dermoscopy images,” International Journal of Machine Learning and Computing, vol. 8, no. 1, 2018, pp. 61–68.
E. M. Senan and M. E. Jadhav, “Analysis of dermoscopy images by using ABCD rule for early detection of skin cancer,” Global Transitions Proceedings, vol. 2, no. 1, 2021, pp. 1–7.
J. Kawahara, S. Daneshvar, G. Argenziano and G. Hamarneh, “Seven-point checklist and skin lesion classification using multitask multimodal neural nets,” IEEE journal of biomedical and health informatics, vol. 23, no. 2, 2018, pp. 538–546.
G. Di Leo, A. Paolillo, P. Sommella and G. Fabbrocini, “Automatic diagnosis of melanoma: a software system based on the 7-point check-list,” 43rd Hawaii international conference on system sciences, 2010, pp. 1–10.
X. Sun, J. Yang, M. Sun and K. Wang, “A benchmark for automatic visual classification of clinical skin disease images,” European Conference on Computer Vision, 2016, pp. 206–222.
X. Dai, I. Spasic´, B. Meyer, S. Chapman and F. Andres, “Machine learning on mobile: An on-device inference app for skin cancer detection,” Fourth International Conference on Fog and Mobile Edge Computing, 2019, pp. 301–305.
K. M. Hosny, M. A. Kassem and M. M. Fouad, “Classification of skin lesions into seven classes using transfer learning with AlexNet,” Journal of digital imaging, vol. 33, no. 5, 2020, pp. 1325–1334.
M. A. Kassem, K. M. Hosny and M. M. Fouad, “Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning,” IEEE Access, vol. 8, 2020, pp. 114822–114832.
A. Lucieri, M. N. Bajwa, S. A. Braun, M. I. Malik, A. Dengel, and S. Ahmed, “On interpretability of deep learning based skin lesion classifiers using concept activation vectors,” International Joint Conference on Neural Networks, 2020, pp. 1–10.
X. Li, J. Wu, E. Z. Chen and H. Jiang, “What evidence does deep learning model use to classify skin lesions?,” arXiv preprint arXiv:1811.01051, 2018.
P. Xie, K. Zuo, J. Liu, M. Chen, S. Zhao, W. Kang and F. Li, “Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network,” Journal of Healthcare Engineering, vol. 2021, 2021, pp. 1-7.
I. A. Alfi, M. M. Rahman, M. Shorfuzzaman and A. Nazir, “A Non-Invasive Interpretable Diagnosis of Melanoma Skin Cancer Using Deep Learning and Ensemble Stacking of Machine Learning Models,” Diagnostics, vol. 12, no. 3, 2022, pp. 1–18.
C. Chen, O. Li, C. Tao, A. J. Barnett, J. Su and C. Rudin, “This looks like that: deep learning for interpretable image recognition,” arXiv preprint arXiv:1806.10574, 2018.
A. J. Barnett, F. R. Schwartz, C. Tao, C. Chen, Y. Ren, J. Y. Lo, and C. Rudin, “IAIA-BL: A case-based interpretable deep learning model for classification of mass lesions in digital mammography,” arXiv preprint arXiv:2103.12308, 2021.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255.
Y. Zhang, K. Song, Y. Sun, S. Tan, and M. Udell, “” why should you trust my explanation?” understanding uncertainty in lime explanations,” arXiv preprint arXiv:1904.12991, 2019, pp. 1-10.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
