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LSNet: a deep learning based method for skin lesion classification using limited samples and transfer learning

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Abstract

When analyzing skin lesion image data using deep learning, the lack of a sufficient amount of effective training data poses a challenge. Although transfer learning can alleviate the problem of a small amount of data, the difference between the source data and the target data makes the tranfer learning missing some key knowledge. It is important to find the key knowledge neglected by transfer learning. This paper argues that this key knowledge is contained in challenging samples. We propose a novel method named as Limited Samples Network (LSNet) to search challenging samples and strengthen the learning of them. Specifically, LSNet utilizes patch-based structured input and employs pseudoinverse learning autoencoder to quickly obtain position-sensitive loss. Challenging samples can be obtained by searching for position-sensitive loss. Subsequently, challenging samples-augmented transfer learning is employed to enhance the classification performance of deep learning models on skin lesion datasets with limited samples. We carry comparison experiment with the existing state-of-the-art method. Experiments are carried out on the ISIC 2017, ISIC 2018 and ISIC 2019 skin lesion datasets. The results demonstrate that our training strategy significantly improves the vanilla transfer learning procedure for different types of pre-trained DCNNs. In particular, our method achieves state-of-the-art performance on different skin lesion datasets without using any extra training data.

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Data Availability Statement

The datasets analyzed during the current study are available in the repository: https://challenge.isic-archive.com/data/, https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database

Notes

  1. https://challenge.isic-archive.com/leaderboards/2017

  2. ACC: The proportion of correct predictions among the total number of cases evaluated.

  3. BACC: The accuracies of each category weighted by the category prevalence.

  4. https://challenge.isic-archive.com/leaderboards/2018

  5. https://challenge.isic-archive.com/leaderboards/2019

References

  1. Hu Z, Tang J, Wang Z, Zhang K, Zhang L (2018) Deep learning for image-based cancer detection and diagnosis - a survey. Pattern Recogn 83:134–149

    Article  Google Scholar 

  2. Li H, Pan Y, Zhao J, Zhang L (2021) Skin disease diagnosis with deep learning: a review. Neurocomputing 464:364–393

    Article  Google Scholar 

  3. Gessert N, Sentker T, Madesta F, Schmitz R, Kniep H, Baltruschat I, Werner R, Schlaefer A (2020) Skin lesion classification using cnns with patch-based attention and diagnosis-guided loss weighting. IEEE Trans Biomed Eng 67(2):495–503

    Article  Google Scholar 

  4. Yao P, Shen S, Xu M, Liu P, Zhang F, **ng J, Shao P, Kaffenberger B, Xu RX (2021) Single model deep learning on imbalanced small datasets for skin lesion classification. IEEE Trans Med Imaging 41(5):1242–1254

    Article  Google Scholar 

  5. Ferhat B (2023) Skin lesion classification on dermatoscopic images using effective data augmentation andpre-trained deeplearning approach. Multimed Tools App 82:18985–19003

    Article  Google Scholar 

  6. Syed QG, Oge M (2023) Skin lesion analysis using generative adversarial networks: a review. Multimed Tools App 82:30065–30106

    Article  Google Scholar 

  7. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Article  Google Scholar 

  8. Haenssle H, Fink A, Schneiderbauer C, Toberer R, Buhl F (2018) Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol Off J Eur Soc Med Oncol 29(8):1836–1842

    Article  Google Scholar 

  9. Bissoto A, Perez F, Valle E, Avila S (2018) Skin lesion synthesis with generative adversarial networks. In: ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, pp 294–302. Springer

  10. Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B (2022) High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10684–10695

  11. Guo P, Lyu MR (2004) A pseudoinverse learning algorithm for feedforward neural networks with stacked generalization applications to software reliability growth data. Neurocomputing 56:101–121

    Article  Google Scholar 

  12. Celebi ME et al (2007) A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph 31(6):362–373

    Article  Google Scholar 

  13. Ramlakhan K, Shang Y (2011) A mobile automated skin lesion classification system. In: 2011 IEEE 23rd International conference on tools with artificial intelligence, pp 138– 141 IEEE

  14. Maglogiannis I, Doukas CN (2009) Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inform Technol Biomed A Pub IEEE Eng Med Bio Soc 13(5):721–33

    Article  Google Scholar 

  15. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  16. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  17. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  18. ** model for automated skin lesion segmentation and classification. IEEE Trans Med Imaging 39(7):2482–2493

    Article  Google Scholar 

  19. Yu L, Chen H, Dou Q, Qin J, Heng P-A (2016) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 36(4):994–1004

    Article  Google Scholar 

  20. Zhang B et al (2020) Short-term lesion change detection for melanoma screening with novel siamese neural network. IEEE Trans Med Imaging 40(3):840–851

    Article  Google Scholar 

  21. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  22. Brinker TJ, Hekler A, Enk AH, Klode J et al (2019) A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task. Eur J Cancer 111:148–154

    Article  Google Scholar 

  23. Zhuang J, Li W, Manivannan S, Wang R, Zhang JJ-G, Pan J, Jiang G, Yin Z (2018) Skin lesion analysis towards melanoma detection using deep neural network ensemble. ISIC Challenge 2018:2

    Google Scholar 

  24. Li KM, Li EC (2018) Skin lesion analysis towards melanoma detection via end-to-end deep learning of convolutional neural networks. Sensors 18(2). https://doi.org/10.3390/s18020556

  25. Mohammed KA, Baljit S, Avez R (2018) Skin lesion classification and segmentation for imbalanced classes using deep learning. International Skin Imaging Collaboration (ISIC) Challenge on Skin Image Analysis for Melanoma Detection. MICCAI

  26. Barata C, Marques JS, Celebi ME (2019) Deep attention model for the hierarchical diagnosis of skin lesions. IEEE Conference on computer vision and pattern recognition workshops, CVPR Workshops 2019, Long Beach, CA, USA, June 16–20, 2019. IEEE, Long Beach, pp 2757–2765

    Google Scholar 

  27. Harangi B (2018) Skin lesion classification with ensembles of deep convolutional neural networks. J Biomed Inform 86:25–32

    Article  Google Scholar 

  28. Matsunaga K, Hamada A, Minagawa A, Koga H (2017) Image classification of melanoma, nevus and seborrheic keratosis by deep neural network ensemble. ar**v:1703.03108

  29. Diaz IG (2017) Incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions. IEEE J Biomed Health Inform 23(2):547–559

    Article  Google Scholar 

  30. Zhang J, **e Y, **a Y, Shen C (2019) Attention residual learning for skin lesion classification. IEEE Trans Med Imaging 38(9):2092–2103

    Article  Google Scholar 

  31. Nozdryn-Plotnicki A, Yap J, Yolland W (2018) Ensembling convolutional neural networks for skin cancer classification. International Skin Imaging Collaboration (ISIC) Challenge on Skin Image Analysis for Melanoma Detection. MICCAI

  32. Gessert N, Sentker T, Madesta F, Schmitz R, Kniep H, Baltruschat I, Werner R, Schlaefer A (2018) Skin lesion diagnosis using ensembles, unscaled multi-crop evaluation and loss weighting. ar**v:1808.01694

  33. Guo P, Chen CP, Sun Y (1995) An exact supervised learning for a three-layer supervised neural network. In: Proceedings of 1995 international conference on neural information processing, pp 1041–1044

  34. Guo P, Lyu MR, Mastorakis N (2001) Pseudoinverse learning algorithm for feedforward neural networks. Advances in Neural Networks and Applications, 321–326

  35. Codella NC, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra N, Kittler H et al (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018), pp 168–172. IEEE

  36. Tschandl P, Rosendahl C, Kittler H (2018) The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci data 5:180161

    Article  Google Scholar 

  37. Kingma D, Ba J (2014) Adam: a method for stochastic optimization. In: ICLR

  38. Zhang H, Wu C, Zhang Z, Zhu Y, Lin H, Zhang Z, Sun Y, He T, Mueller J, Manmatha R et al (2020) Resnest: Split-attention networks. In: The IEEE Conference on computer vision and pattern recognition (CVPR)

  39. Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning, pp 6105–6114. PMLR

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  1. Department of Systems Science, College of Automation, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China

    **aodan Deng

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Correspondence to **aodan Deng.

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Deng, X. LSNet: a deep learning based method for skin lesion classification using limited samples and transfer learning. Multimed Tools Appl 83, 61469–61489 (2024). https://doi.org/10.1007/s11042-023-17975-2

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