Abstract
Deep learning based methodologies, especially convolutional neural networks, recently contributed to the field of medical image analysis. Diseases are recognized by passing digital images into end-to-end networks. However, identifying visual patterns making significant effects on prediction results is recently a challenge for scientists. A remarkable number of researches, in the field of Explainable AI, are conducted, including data explainability, model explainability, and post-hoc explainability. In the research, the authors utilize a deconvolutional neural network to reconstruct original skin lesion images from learned feature vectors to emphasize visual patterns mostly contributing to recognition results. Additionally, a feature enhancement technique based on blending is proposed to verify the effect of learned features. Experiments conducted in the HAM10000 data set show that the proposed methodology is promising for data explainability.
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References
Ahern, I., Noack, A., Guzman-Nateras, L., Dou, D., Li, B., Huan, J.: NormLIME: a new feature importance metric for explaining deep neural networks (2020)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009). https://doi.org/10.1109/CVPR.2009.5206848
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https://doi.org/10.18653/v1/N19-1423. https://aclanthology.org/N19-1423
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021). https://openreview.net/forum?id=YicbFdNTTy
El Bekri, N., Kling, J., Huber, M.F.: A study on trust in black box models and post-hoc explanations. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J.A., Quintián, H., Corchado, E. (eds.) SOCO 2019. AISC, vol. 950, pp. 35–46. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-20055-8_4
Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., Giannotti, F.: Local rule-based explanations of black box decision systems (2018)
Guo, M.H., et al.: Attention mechanisms in computer vision: a survey. Comput. Vis. Media 8(3), 331–368 (2022). https://doi.org/10.1007/s41095-022-0271-y. https://www.sciopen.com/article/10.1007/s41095-022-0271-y
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp. 770–778. IEEE, June 2016. https://doi.org/10.1109/CVPR.2016.90. http://ieeexplore.ieee.org/document/7780459
Ribeiro, M., Singh, S., Guestrin, C.: “why should I trust you?”: explaining the predictions of any classifier. In: DeNero, J., Finlayson, M., Reddy, S. (eds.) Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 97–101. Association for Computational Linguistics, San Diego, California, June 2016. https://doi.org/10.18653/v1/N16-3020. https://aclanthology.org/N16-3020
Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks (2020)
Tschandl, P.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Harvard Dataverse (2018). https://doi.org/10.7910/DVN/DBW86T
Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models, pp. 1–13 (2018)
Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
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Nguyen, TA., Le, B. (2024). Feature Explainability and Enhancement for Skin Lesion Image Analysis. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14811. Springer, Cham. https://doi.org/10.1007/978-3-031-70819-0_18
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