Incremental learning approach for semantic segmentation of skin histology images

Posted on
incremental-learning-approach-for-semantic-segmentation-of-skin-histology-images
Incremental learning approach for semantic segmentation of skin histology images

References

  1. Fullman, N. et al. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016. The Lancet 390(10100), 1423–1459 (2017).

    Google Scholar 

  2. Kocarnik, J. M. et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the global burden of disease study 2019. JAMA Oncol. 8(3), 420–444 (2022).

    Google Scholar 

  3. Aggarwal, P., Knabel, P. & Fleischer, A. B. Jr. United States burden of melanoma and non-melanoma skin cancer from 1990 to 2019. *J. Am. Acad. Dermatol.* 85(2), 388–395 (2021).

    Google Scholar 

  4. Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. CA Cancer J. Clin. 74(1), 12–49 (2024).

    Google Scholar 

  5. Hayat, M. A. Brain Metastases from Primary Tumors, Volume 3: Epidemiology, Biology, and Therapy of Melanoma and Other Cancers. (Academic Press, 2016).

  6. Feldman, A. T. & Wolfe, D. Tissue processing and hematoxylin and eosin staining. Histopathology: Methods and Protocols, pp. 31–43, (Springer, 2014).

  7. Fischer, A. H., Jacobson, K. A., Rose, J. & Zeller, R. Hematoxylin and eosin staining of tissue and cell sections. Cold Spring Harb. Protoc. 2008 (5), pdb–prot4986. (Cold Spring Harbor Laboratory Press, 2008).

  8. Gouda, W., Sama, N. U., Al-Waakid, G., Humayun, M. & Jhanjhi, N. Z. Detection of skin cancer based on skin lesion images using deep learning. Healthcare 10 (7), 1183. (MDPI, 2022).

  9. Singh, R. P. et al. Current challenges and barriers to real-world artificial intelligence adoption for the healthcare system, provider, and the patient. Transl. Vis. Sci. Technol. 9(2), 45–45 (2020).

    Google Scholar 

  10. Maron, R. C. et al. Artificial intelligence and its effect on dermatologists’ accuracy in dermoscopic melanoma image classification: web-based survey study. J. Med. Internet Res. 22(9), e18091 (2020).

    Google Scholar 

  11. Wang, S. et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). Eur. Radiol. 31, 6096–6104 (2021).

    Google Scholar 

  12. Ström, P. et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 21(2), 222–232 (2020).

    Google Scholar 

  13. Brinker, T. J. et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur. J. Cancer 113, 47–54 (2019).

    Google Scholar 

  14. Leo, J. & Kalita, J. Survey of continuous deep learning methods and techniques used for incremental learning. Neurocomputing 582, 127545 (2024).

    Google Scholar 

  15. Li, Z. & Hoiem, D. Learning without forgetting. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 2935–2947 (2017).

    Google Scholar 

  16. Dhar, P., Singh, R. V., Peng, K.-C., Wu, Z. & Chellappa, R. Learning without memorizing. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp. 5138–5146 (2019).

  17. Wu, Y. et al. Incremental classifier learning with generative adversarial networks. arXiv preprint arXiv:1802.00853 (2018).

  18. Cermelli, F., Mancini, M., Bulo, S. R., Ricci, E. & Caputo, B. Modeling the background for incremental learning in semantic segmentation. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp. 9233–9242 (2020).

  19. Michieli, U. & Zanuttigh, P. Incremental learning techniques for semantic segmentation. In: Proc. IEEE/CVF Int. Conf. Comput. Vis. Workshops, pp. 0–0 (2019).

  20. Cermelli, F., Fontanel, D., Tavera, A., Ciccone, M. & Caputo, B. Incremental learning in semantic segmentation from image labels. In: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp. 4371–4381 (2022).

  21. Xie, E. et al. SegFormer: Simple and efficient design for semantic segmentation with transformers. Adv. Neural Inf. Process. Syst. 34, 12077–12090 (2021).

    Google Scholar 

  22. Kosgiker, G. M. & Deshpande, A. A novel SEGCAP algorithm based enhanced segmentation of dermoscopic images of interest. Mater. Today Proc. 51, 779–787 (2022).

    Google Scholar 

  23. Imran, M., Tiwana, M. I., Mohsan, M. M., Alghamdi, N. S. & Akram, M. U. Transformer-based framework for multi-class segmentation of skin cancer from histopathology images. Front. Med. 11, 1380405 (2024).

    Google Scholar 

  24. Hameed, N., Shabut, A. M., Ghosh, M. K. & Hossain, M. A. Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques. Expert Syst. Appl. 141, 112961 (2020).

    Google Scholar 

  25. Moradi, N. & Mahdavi-Amiri, N. Multi-class segmentation of skin lesions via joint dictionary learning. Biomed. Signal Process. Control 68, 102787 (2021).

    Google Scholar 

  26. Vaswani, A. Attention is all you need. In: Adv. Neural Inf. Process. Syst. (2017).

  27. Dosovitskiy, A. et al. An image is worth 16(times)16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).

  28. Wang, W. et al. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In: Proc. IEEE/CVF Int. Conf. Comput. Vis., pp. 568–578 (2021).

  29. Islam, M. A., Jia, S. & Bruce, N. D. B. How much position information do convolutional neural networks encode? arXiv preprint arXiv:2001.08248 (2020).

  30. Chu, X., Tian, Z., Zhang, B., Wang, X. & Shen, C. Conditional positional encodings for vision transformers. arXiv preprint arXiv:2102.10882 (2021).

  31. Song, Y., Zhang, P., Huang, W., Zha,Y., You, T. & Zhang, Y. Parallel-circuitized’ distillation for dense object detection. Displays, p. 102587, (2023).

  32. Thomas, S. M., Lefevre, J. G., Baxter, G. & Hamilton, N. A. Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer. Med. Image Anal. 68, 101915 (2021).

    Google Scholar 

  33. Imran, M. et al. Two-dimensional hybrid incremental learning (2DHIL) framework for semantic segmentation of skin tissues. Image Vis. Comput. 148, 105147 (2024).

    Google Scholar 

Download references