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A Prior-mask-guided Few-shot Learning for Skin Lesion Segmentation

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Abstract

The incidence of skin cancer, which has high mortality, is growing rapidly worldwide. Early detection of skin lesions is crucial for timely diagnosis and treatment to improve the patient survival rate. Computer vision technology based on deep convolutional neural network requires a large amount of labelled data. The cost of data acquisition and annotation is relatively high, especially for skin cancer segmentation tasks. Therefore, we propose a few-shot segmentation network for skin lesion segmentation, which requires only a few pixel-level annotations. First, the co-occurrence region between the support image and query image is obtained, which is used as a prior mask to exclude irrelevant background regions. Second, the results are concatenated and sent to the inference module to predict segmentation of the query image. Third, the proposed network is retrained by reversing the support and query role, which benefits from the symmetrical structure. Extensive experiments performed on ISIC-2017, ISIC-2019, and PH2 demonstrate that our method forms a promising framework for few-shot segmentation of skin lesion.

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References

  1. Pathan S, Prabhu KG, Siddalingaswamy P (2018) Techniques and algorithms for computer aided diagnosis of pigmented skin lesions A review. Biomed Signal Process Control 39:237–262

    Article  Google Scholar 

  2. Baig Ramsha et al (2020) Deep Learning Approaches Towards Skin Lesion Segmentation and Classification from Dermoscopic Images-A Review. Current Med Imaging 16(5):513–533

    Article  Google Scholar 

  3. Zhang W, Li R, Deng H, Wang L, Lin W, Jis S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108:214–224

    Article  Google Scholar 

  4. Yap MH, Pons G, Martí J, Ganau S, Sentís M, Zwiggelaar R, Davison AK, Martí R (2017) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 22(4):1218–1226

    Article  Google Scholar 

  5. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). pp 234-241

  6. Yuan Y, Chao M, Lo Y-C (2017) Automatic skin lesion segmentation using deep fully convolutional networks with Jaccard distance. IEEE Trans Med Imag 36(9):1876–1886

    Article  Google Scholar 

  7. Yu L, Chen H, Dou Q, Qin J, Heng P-A (2017) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imag 36(4):994–1004

    Article  Google Scholar 

  8. Goyal Manu et al (2019) Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access 8:4171–4181

    Article  Google Scholar 

  9. Feng S et al (2020) CPFNet: context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging 99:1

    Google Scholar 

  10. Xie Y et al (2020) A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans Med Imaging 99:1

    Google Scholar 

  11. Uijlings JRR et al. (2013) Selective search for object recognition. In: Proceedings of International Journal of Computer Vision pp 154-171

  12. Lin Di et al. (2016) Scribblesup:Scribble-supervised convolutional networks for semantic segmentation. In: Proceedings of CVPR, pp 3159-3167

  13. Bearman A, Russakovsky O, Ferrari V, Fei-Fei L, et al (2016) Whats the point: Semantic segmentation with point supervision. In: Proceedings of ECCV. pp 549–565

  14. Huang Z, Wang X, Wang J, Liu W, Wang J (2018) Weakly supervised semantic segmentation network with deep seeded region growing. In: Proceedings of CVPR. pp 7104–7023

  15. Junsheng, X, Huahu X, Honghao G, Minjie B, Yang L (2020) A weakly supervised semantic segmentation network by aggregating seed cues: the multi-objects proposal generation perspective. ACM Trans Multimed Comput Commun https://doi.org/10.1145/3419842

  16. Xu G, Song Z, Sun Z, Ku C, Yang Z, Liu C, Xu W Camel (2019) A weakly supervised learning framework for histopathology image segmentation. In: Proceedings of the IEEE international conference on computer vision pp 10682-10691

  17. Huang Y, Chung AC (2019) Evidence localization for pathology images using weakly supervised learning. In: International conference on medical image computing and computer-assisted intervention pp. 613-621

  18. Hospedales T et al (2020) Meta-learning in neural networks: A survey. arXiv:2004.05439

  19. Jintai C, Haochao Y, Xuechen L, Jingjing G, Ruiwei F, Tingting C, Honghao G, Jian W (2020) A transfer learning based super-resolution microscopy for biopsy slice images: the joint methods perspective. In: IEEE/ACM transactions on computational biology and bioinformatics (TCBB)

  20. Lin B, Deng S, Gao H, Yin J (2020) A multi-scale activity transition network for data translation in EEG signals decoding. In: IEEE/ACM transactions on computational biology and bioinformatics. https://doi.org/10.1109/TCBB.2020.3024228

  21. Shaban, A et al (2017) One-shot learning for semantic segmentation. arXiv:1709.03410

  22. Zhang Xiaolin et al (2020) Sg-one: Similarity guidance network for one-shot semantic segmentation. IEEE Trans Cybern 99:1–11

    Google Scholar 

  23. Rakelly K et al (2018) Conditional networks for few-shot semantic segmentation. In Proceedings of ICLR

  24. Zhang C et al (2019) Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In: Proceedings of CVPR pp 5217-5226

  25. Mondal AK, Jose D, Christian D (2018) Few-shot 3d multi-modal medical image segmentation using generative adversarial learning. arXiv preprint arXiv:1810.12241

  26. Rutter EM, Lagergren JH, Flores KB (2019) A convolutional neural network method for boundary optimization enables few-shot learning for biomedical image segmentation. In: Domain adaptation and representation transfer and medical image learning with less labels and imperfect data, pp 190–198

  27. Abbasi NR, Shaw HM, Rigel DS, Friedman RJ, McCarthy WH, Osman I, Kopf AW, Polsky D (2004) Early diagnosis of cutaneous melanoma: revisiting the abcd criteria. Jama 292(22):2771–2776

    Article  Google Scholar 

  28. Xie F, Bovik AC (2013) Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm. Pattern Recognit 46(3):1012–1019

    Article  Google Scholar 

  29. Peruch F, Bogo F, Bonazza M, Cappelleri V-M, Peserico E (2014) Simpler, faster, more accurate melanocytic lesion segmentation through meds. IEEE Trans Biomed Eng 61(2):557–565

    Article  Google Scholar 

  30. Abbas Q, Celebi ME, García IF (2012) Skin tumor area extraction using an improved dynamic programming approach. Skin Res Technol 18(2):133–142

    Article  Google Scholar 

  31. Zhou H, Schaefer G, Celebi ME, Lin F, Liu T (2011) Gradient vector flow with mean shift for skin lesion segmentation. Comput Med Imaging Graph 35(2):121–127

    Article  Google Scholar 

  32. Wang H, Moss RH, Chen X, Stanley RJ, Stoecker WV, Celebi ME, Malters JM et al (2011) Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images. Comput Med Imaging Graph 35(2):116–120

    Article  Google Scholar 

  33. Al-masni MA, Al-antari MA, Choi M-T, Han S-M, Kim T-S (2018) Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput Methods Programs Biomed 162:221–231

    Article  Google Scholar 

  34. Wang Y et al (2019) Few-shot learning: A survey. arXiv:1904.05046

  35. Finn C, Pieter A, Sergey L (2017) Model-agnostic meta-learning for fast adaptation of deep networks. arXiv:1703.03400

  36. Ravi S, Hugo L (2017) Optimization as a model for few-shot learning. In: Proceedings of ICLR

  37. Mishra N et al (2017) A simple neural attentive meta-learner. arXiv:1707.03141

  38. Qiao S et al (2018) Few-shot image recognition by predicting parameters from activations. In: Proceedings of CVPR. pp 7229-7238

  39. Koch G, Richard Z, Ruslan S (2015) Siamese neural networks for one-shot image recognition. In Proceedings ICML

  40. Vinyals O, et al (2016) Matching networks for one shot learning. In: Proceedings of NeurIPS pp 3630-3638

  41. Snell J, Kevin S, Richard Z (2017) Prototypical networks for few-shot learning. In: Proceedings of NeurIPS. pp 4077-4087

  42. Sung F et al (2018) Learning to compare: Relation network for few-shot learning. In Proceedings of CVPR. pp 1199-1208

  43. Garcia V, Joan B (2017) Few-shot learning with graph neural networks. arXiv:1711.04043

  44. Dong N, Xing EP (2018) Few-Shot Semantic Segmentation with Prototype Learning. In: Proceedings of BMVC Vol. 3. No. 4

  45. Wang K et al (2019) Panet: Few-shot image semantic segmentation with prototype alignment. In: Proceedings of ICCV pp 9197-9206

  46. Liu W, Zhang C, Lin G, Liu F (2020) CRNet: Cross-Reference Networks for Few-Shot Segmentation. In: Proceedings of CVPR. pp 4165-4173

  47. Tian Z et al (2020) Prior guided feature enrichment network for few-shot segmentation. IEEE Ann History Comput 01:1

    Google Scholar 

  48. Varun J, Deqing S et al (2018) Superpixel sampling networks. In: Proceedings of ECCV pp 352-368

  49. Codella NCF et al, 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: Proceedings of IEEE 15th international symposium on biomedical imaging

  50. Marc C, Codella NCF et al (2019) BCN20000: DERMOSCOPIC LESIONS IN THE WILD:A challenge at the 2019 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1908.0228

  51. Mendonça T, Ferreira PM, Marques JS, Marcal ARS, Rozeira J (2013) PH2—A dermoscopic image database for research and benchmarking. In: Proceedings of 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC) pp 5437–5440

  52. Long J, Evan S, Trevor D (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of CVPR. pp 3431-3440

  53. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, People’s Republic of China

    Junsheng Xiao, Huahu Xu, Chen Cheng & HongHao Gao

  2. Information Office of Shanghai, Shanghai, 200444, People’s Republic of China

    Huahu Xu

  3. Henan Provincial Hospital of Traditional Chinese Medicine, Zhengzhou, 450046, People’s Republic of China

    Wei Zhao

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  1. Junsheng Xiao
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  2. Huahu Xu
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  3. Wei Zhao
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  4. Chen Cheng
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  5. HongHao Gao
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Correspondence to HongHao Gao.

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Xiao, J., Xu, H., Zhao, W. et al. A Prior-mask-guided Few-shot Learning for Skin Lesion Segmentation. Computing 105, 717–739 (2023). https://doi.org/10.1007/s00607-021-00907-z

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