An Overview of Few-Shot Learning Methods in Analysis of Histopathological Images

1. Gurcan, M., Boucheron, L., Can, A., Madabhushi, A., Rajpoot, N., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 147–71 (2009)

   Google Scholar 

2. Parnami A., Lee, M.: Learning from few examples: a summary of approaches to few-shot learning (2022). arxiv:2203.04291

3. Song, Y., Wang, T., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: evolution, applications, challenges, and opportunities (2022). arxiv:2205.06743

4. Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. 53(3). https://doi.org/10.1145/3386252

5. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks (2017). arxiv:1703.03400

6. So, C.: Exploring meta learning: Parameterizing the learning-to-learn process for image classification. In: International conference on artificial intelligence in information and communication (ICAIIC) 2021, pp. 199–202

   Google Scholar 

7. Singh, R., Bharti, V., Purohit, V., Kumar, A., Singh, A.K., Singh, S.K.: Metamed: few-shot medical image classification using gradient-based meta-learning. Pattern Recogn. 120, 108111 (2021). https://www.sciencedirect.com/science/article/pii/S0031320321002983

8. Koch, G.R.: Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop (2015)

   Google Scholar 

9. Vinyals, O., Blundell, C., Lillicrap, T., kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc., (2016). https://proceedings.neurips.cc/paper/2016/file/90e1357833654983612fb05e3ec9148c-Paper.pdf

10. Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2495–2504 (2020)

    Google Scholar 

11. Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning (2017). arxiv:1703.05175

12. Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)

    Article  Google Scholar 

13. Qin, T., Li, W., Shi, Y., Gao, Y.: Diversity helps: unsupervised few-shot learning via distribution shift-based data augmentation (2020) arxiv:2004.05805

14. Hariharan, B., Girshick, R.: Low-shot visual recognition by shrinking and hallucinating features (2016). arxiv:1606.02819

15. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K. (eds.) Advances in neural information processing systems, vol. 27. Curran Associates, Inc., (2014). https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf

16. Mirza, M., Osindero, S.: Conditional generative adversarial nets (2014). arxiv:1411.1784

17. Kumar, N., Gupta, S., Gupta, R.: Whole slide imaging (wsi) in pathology: current perspectives and future directions. J. Digit. Imaging 4, 1034–1040 (2020)

    Article  Google Scholar 

18. Amin, S., Mori, T., Itoh, T.: A validation study of whole slide imaging for primary diagnosis of lymphoma. Pathol. Int. 69(6), 341–349 (2019). https://onlinelibrary.wiley.com/doi/abs/10.1111/pin.12808

19. Fox, H.: Is h &e morphology coming to an end? J. Clin. Pathol. 1, 38–40 (2000)

    Article  Google Scholar 

20. Alturkistani, H., Tashkandi, F., Mohammedsaleh, Z.: Histological stains: a literature review and case study. Glob. J. Health Sci. 3, 72–9 (2015)

    Article  Google Scholar 

21. Libard, D., Cerjan, S., Alafuzoff, I.: Characteristics of the tissue section that influence the staining outcome in immunohistochemistry. Histochem. Cell Biol. 151, 91–96 (2019)

    Google Scholar 

22. Chen, P., Liang, Y., Shi, X., Yang, L., Gader, P.: Automatic whole slide pathology image diagnosis framework via unit stochastic selection and attention fusion Neurocomputing 312–325 (2021)

    Google Scholar 

23. Kriegsmann, M., Warth, A.: What is better/reliable, mitosis counting or ki67/mib1 staining? Transl. Lung Cancer Res. 5, 543–546 (2016)

    Article  Google Scholar 

24. Wenbin, H., Ting, L., Yongjie, H., Wuyi, M., Jinguang, D., Yinxia, L., Yuan, Y., Leijie, W., Zhiwen, J., Yongqiang, W., Jie, Y., Chen, C.: A review: the detection of cancer cells in histopathology based on machine vision. Comput. Biol. Med. (2022)

    Google Scholar 

25. Chao, S., Belanger, D.: Generalizing few-shot classification of whole-genome doubling across cancer types. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 3382–3392 (2021)

    Google Scholar 

26. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2009, pp. 248–255

    Google Scholar 

27. Fagerblom, F., Stacke, K., Molin, J.: Combatting out-of-distribution errors using model-agnostic meta-learning for digital pathology. In: Medical imaging (2021)

    Google Scholar 

28. Litjens, G.J.S., Bándi, P., Bejnordi, B.E., Geessink, O.G.F., Balkenhol, M.C.A., Bult, P., Halilovic, A., Hermsen, M., van de Loo, R., Vogels, R., Manson, Q.F., Stathonikos, N., Baidoshvili, A., van Diest, P., Wauters, C.A., van Dijk, M., van der Laak, J.: 1399 h &e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset. GigaScience 7 (2018)

    Google Scholar 

29. Lindman, K., Rose, J.F., Lindvall, M., Stadler, C.B.: Ovary data from the visual Sweden project droid (2019). https://datahub.aida.scilifelab.se/10.23698/aida/drov

30. Yuan, Z., Esteva, A., Xu, R.: Metahistoseg: a python framework for meta learning in histopathology image segmentation. In: Deep Generative Models, and Data Augmentation, Labelling, and Imperfections: first Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, Proceedings. Springer, Berlin, Heidelberg, pp. 268–275 (2021). https://doi.org/10.1007/978-3-030-88210-5_27

31. Zhang, C., Cui, Q., Ren, S.: Few-shot medical image classification with MAML based on dice loss. In: 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA), pp. 348–351 (2022)

    Google Scholar 

32. Lau, J.J., Gayen, S., Ben Abacha, A., Demner-Fushman, D.: A dataset of clinically generated visual questions and answers about radiology images. Sci. Data 5, 180251 (2018)

    Google Scholar 

33. He, X., Zhang, Y., Mou, L., Xing, E., Xie, P.: Pathvqa: 30000+ questions for medical visual question answering (2020). arxiv:2003.10286

34. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization (2017). arxiv:1710.09412

35. DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout (2017). arxiv:1708.04552

36. Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Google Scholar 

37. Wen, Q., Yan, J., Liu, B., Meng, D., Li, S.: A meta-learning method for histopathology image classification based on LSTM-model. In: Tenth international conference on graphic and image processing (ICGIP 2018) (2019)

    Google Scholar 

38. Medela, A., Picon, A., Saratxaga, C.L., Belar, O., Cabezón, V., Cicchi, R., Bilbao, R., Glover, B.: Few shot learning in histopathological images: reducing the need of labeled data on biological datasets. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1860–1864 (2019)

    Google Scholar 

39. Kather, J., Weis, C.-A., Bianconi, F., Melchers, S., Schad, L., Gaiser, T., Marx, A., Zöllner, F.: Multi-class texture analysis in colorectal cancer histology. Sci. Rep. 6, 27988 (2016)

    Google Scholar 

40. Sikaroudi, M., Safarpoor, A., Ghojogh, B., Shafiei, S., Crowley, M., Tizhoosh, H.: Supervision and source domain impact on representation learning: a histopathology case study. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1400–1403 (2020)

    Google Scholar 

41. Medela, A., Picon, A.: Constellation loss: improving the efficiency of deep metric learning loss functions for optimal embedding (2019). arxiv:1905.10675

42. Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc., (2016). https://proceedings.neurips.cc/paper/2016/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf

43. Teh, E.W., Taylor, G.W.: Learning with less data via weakly labeled patch classification in digital pathology (2019). arxiv:1911.12425

44. Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant cnns for digital pathology (2018). arxiv:1806.03962

45. Movshovitz-Attias, Y., Toshev, A., Leung, T.K., Ioffe, S., Singh, S.: No fuss distance metric learning using proxies (2017). arxiv:1703.07464

46. Yang, J., Chen, H., Yan, J., Chen, X., Yao, J.: Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning (2022). arxiv:2202.09059

47. Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers (2021). arxiv:2104.02057

48. Kather, J.N., Halama, N., Marx, A.: 100,000 histological images of human colorectal cancer and healthy tissue (2018). https://doi.org/10.5281/zenodo.1214456

49. Borkowski, A.A., Bui, M.M., Thomas, L.B., Wilson, C.P., DeLand, L.A., Mastorides, S.M.: Lung and colon cancer histopathological image dataset (lc25000) (2019). arxiv:1912.12142

50. Kim, Y.J., Jang, H., Lee, K., Park, S., Min, S.-G., Hong, C., Park, J. H., Lee, K., Kim, J., Hong, W., Jung, H., Liu, Y., Rajkumar, H., Khened, M., Krishnamurthi, G., Yang, S., Wang, X., Han, C.H., Kwak, J.T., Ma, J., Tang, Z., Marami, B., Zeineh, J., Zhao, Z., Heng, P.-A., Schmitz, R., Madesta, F., Rösch, T., Werner, R., Tian, J., Puybareau, E., Bovio, M., Zhang, X., Zhu, Y., Chun, S.Y., Jeong, W.-K., Park, P., Choi, J.: Paip 2019: liver cancer segmentation challenge. Med. Image Anal. 67, 101854 (2021). https://www.sciencedirect.com/science/article/pii/S1361841520302188

51. Shaikh, N.N., Wasag, K., Nie, Y.: Artifact identification in digital histopathology images using few-shot learning. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–4 (2022)

    Google Scholar 

52. Deuschel, J., Firmbach, D., Geppert, C.I., Eckstein, M., Hartmann, A., Bruns, V., Kuritcyn, P., Dexl, J., Hartmann, D., Perrin, D., Wittenberg, T., Benz, M.: Multi-prototype Few-Shot Learning in Histopathology, IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), vol. 2021, pp. 620–628 (2021)

    Google Scholar 

53. Balkenhol, M., Karssemeijer, N., Litjens, G., van der Laak, J., Ciompi, F., Tellez, D.: H &e stain augmentation improves generalization of convolutional networks for histopathological mitosis detection. In: Medical Imaging 2018: digital Pathology, p. 34 (2018)

    Google Scholar 

54. Li, M., Zhao, K., Peng, C., Hobson, P., Jennings, T., Lovell, B.C.: Deep adaptive few example learning for microscopy image cell counting. In: Digital Image Computing: techniques and Applications (DICTA), vol. 2021, pp. 1–7 (2021)

    Google Scholar 

55. Ranjan, V., Sharma, U., Nguyen, T., Hoai, M.: Learning to count everything (2021). arxiv:2104.08391

56. Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.-W., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)

    Article  Google Scholar 

57. Stegmüller, T., Bozorgtabar, B., Spahr, A., Thiran, J.-P.: Scorenet: learning non-uniform attention and augmentation for transformer-based histopathological image classification. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), vol. 2023, pp. 6159–6168 (2023)

    Google Scholar 

58. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16 \(\times \) 16 words: transformers for image recognition at scale (2020). arxiv:2010.11929

59. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need (2017). arxiv:1706.03762

60. Li, M., Li, C., Peng, C., Lovell, B.: Conditioned generative transformers for histopathology image synthetic augmentation (2022). arxiv:2212.09977

61. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017). arxiv:1701.07875

62. Marchesi, M.: Megapixel size image creation using generative adversarial networks (2017). arxiv:1706.00082

63. Yuan, Z., Esteva, A., Xu, R.: Metahistoseg: a python framework for meta learning in histopathology image segmentation (2021). arxiv:2109.14754

64. Saha, S., Choi, O., Whitaker, R.: Few-shot segmentation of microscopy images using gaussian process. In: Huo, Y., Millis, B.A., Zhou, Y., Wang, X., Harrison, A.P., Xu, Z. (eds.) Medical Optical Imaging and Virtual Microscopy Image Analysis, pp. 94–104. Springer Nature Switzerland, Cham (2022)

    Chapter  Google Scholar 

65. Kurmi, Y., Chaurasia, V., Kapoor, N.: Histopathology image segmentation and classification for cancer revelation. Signal Image Video Process 15, 09 (2021)

    Article  Google Scholar 

66. Kim, H., Yoon, H., Thakur, N., Hwang, G., Lee, E., Kim, C., Chong, Y.: Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci. Rep. (1) (2021)

    Google Scholar 

67. He, S., Minn, K.T., Solnica-Krezel, L., Anastasio, M.A., Li, H.: Deeply-supervised density regression for automatic cell counting in microscopy images (2020). arxiv:2011.03683

68. Shakeri, F., Boudiaf, M., Mohammadi, S., Sheth, I., Havaei, M., Ayed, I.B., Kahou, S.E.: Fhist: a benchmark for few-shot classification of histological images (2022). arxiv:2206.00092

69. Moon, S., Sohn, S.S., Zhou, H., Yoon, S., Pavlovic, V., Khan, M.H., Kapadia, M.: HM: hybrid masking for few-shot segmentation. In: Computer Vision-ECCV: 17th European Conference, Tel Aviv, Israel. Proceedings. Part XX, vol. 2022. Springer, pp. 506–523 (2022)

    Google Scholar 

70. Wu, Y., Chanda, S., Hosseinzadeh, M., Liu, Z., Wang, Y.: Few-shot learning of compact models via task-specific meta distillation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 6265–6274 (2023)

    Google Scholar 

71. Li, Z., Hu, Z., Luo, W., Hu, X.: Sabernet: self-attention based effective relation network for few-shot learning. Pattern Recogn. 133, 109024 (2023). https://www.sciencedirect.com/science/article/pii/S0031320322005040

72. Peng, Y., Liu, Y., Tu, B., Zhang, Y.: Convolutional transformer-based few-shot learning for cross-domain hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 16, 1335–1349 (2023)

    Article  Google Scholar 

73. Liu, Y., Shi, D., Lin, H.: Few-shot learning with representative global prototype (2023). https://openreview.net/forum?id=vT2OIobt3pQ

74. Lin, S., Zeng, X., Zhao, R.: Explore the power of dropout on few-shot learning (2023)

    Google Scholar 

75. Da, Q., Huang, X., Li, Z., Zuo, Y., Zhang, C., Liu, J., Chen, W., Li, J., Xu, D., Hu, Z., Yi, H., Guo, Y., Wang, Z., Chen, L., Zhang, L., He, X., Zhang, X., Mei, K., Zhu, C., Lu, W., Shen, L., Shi, J., Li, J., Krishnamurthi, S.S.G., Yang, J., Lin, T., Song, Q., Liu, X., Graham, S., Bashir, R.M.S., Yang, C., Qin, S., Tian, X., Yin, B., Zhao, J., Metaxas, D.N., Li, H., Wang, C., Zhang, S.: Digestpath: a benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system. Med. Image Anal. 80, 102485 (2022). https://www.sciencedirect.com/science/article/pii/S1361841522001323

76. Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550–1560 (2017)

    Article  Google Scholar 

77. Veta, M., Heng, Y.J., Stathonikos, N., Bejnordi, B.E., Beca, F., Wollmann, T., Rohr, K., Shah, M.A., Wang, D., Rousson, M., Hedlund, M., Tellez, D., Ciompi, F., Zerhouni, E., Lanyi, D., Viana, M., Kovalev, V., Liauchuk, V., Phoulady, H.A., Qaiser, T., Graham, S., Rajpoot, N., Sjöblom, E., Molin, J., Paeng, K., Hwang, S., Park, S., Jia, Z., Chang, E.I.-C., Xu, Y., Beck, A.H., van Diest, P.J., Pluim, J.P.: Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge. Med. Image Anal. 54, 111–121 (2019)

    Article  Google Scholar 

78. Veeling, B.S., Linmans, J., Winkens, J., Cohen, T., Welling, M.: Rotation equivariant CNNs for digital pathology (2018)

    Google Scholar 

79. Bejnordi, B.E., Veta, M., Van Diest, P.J., Van Ginneken, B., Karssemeijer, N., Litjens, G., Van Der Laak, J.A., Hermsen, M., Manson, Q.F., Balkenhol, M., Geessink, O.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017). https://doi.org/10.1001/jama.2017.14585

80. Bándi, P., Geessink, O., Manson, Q., Van Dijk, M., Balkenhol, M., Hermsen, M., Ehteshami Bejnordi, B., Lee, B., Paeng, K., Zhong, A., Li, Q., Zanjani, F.G., Zinger, S., Fukuta, K., Komura, D., Ovtcharov, V., Cheng, S., Zeng, S., Thagaard, J., Dahl, A.B., Lin, H., Chen, H., Jacobsson, L., Hedlund, M., Çetin, M., Halici, E., Jackson, H., Chen, R., Both, F., Franke, J., Küsters-Vandevelde, H., Vreuls, W., Bult, P., van Ginneken, B., van der Laak, J., Litjens, G.: From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge. IEEE Trans. Med. Imaging 38(2), 550–560 (2019)

    Google Scholar 

81. Babaie, M., Kalra, S., Sriram, A., Mitcheltree, C., Zhu, S., Khatami, S.A., Rahnamayan, S., Tizhoosh, H.R.: Classification and retrieval of digital pathology scans: a new dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 760–768 (2017)

    Google Scholar 

82. Sirinukunwattana, K., Pluim, J.P.W., Chen, H., Qi, X., Heng, P.-A., Guo, Y.B., Wang, L.Y., Matuszewski, B.J., Bruni, E., Sanchez, U., Böhm, A., Ronneberger, O., Cheikh, B.B., Racoceanu, D., Kainz, P., Pfeiffer, M., Urschler, M., Snead, D.R.J., Rajpoot, N.M.: Gland segmentation in colon histology images: the GLAS challenge contest (2016)

    Google Scholar 

83. Sirinukunwattana, K., Snead, D., Rajpoot, N.: A stochastic polygons model for glandular structures in colon histology images. IEEE Trans. Med. Imaging 34, 05 (2015)

    Article  Google Scholar 

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