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Adversarial learning with data selection for cross-domain histopathological breast Cancer segmentation

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Abstract

Histopathology plays an important role in the clinical diagnosis of breast diseases. Early diagnosis and adjuvant therapy are of great help to patients. With the development of deep learning, fully convolutional networks (FCNs) have achieved remarkable results in the field of segmentation. However, this approach suffers from obtaining sufficient labelled data and underperforms when it comes to a new domain. Recently, adversarial learning becomes prevalent in domain adaptation, which is able to transfer learned knowledge between domains and greatly reduces the workload of labeling. In this paper, we propose a new domain adaptation method, which consists of three steps: adversarial learning, data selection and pseudo-label model refinement. Our method combines the advantages of adversarial learning and pseudo-labelling for domain adaptation. We also introduce a new data selection method to select target domain data with their pseudo-label for model refinement, considering prediction confidence and representativeness, which further strengths the model capability in target domain. We evaluate our method on private HE- and IHC-stained datasets. In order to strength the robustness, the color augmentation is utilized in this paper, the cross-domain prediction performance has been improved from 0.213 Dice to 0.703 Dice. The experimental results show that with only using unlabeled data, the proposed method can achieve 0.846 Dice on target domain, which outperforms the state-of-the-art method by 1.8%.

Highlights

  • The dataset is standard collected and labeled by the professional cooperative doctors and hospital, which means the dataset has a high research and reference value.

  • The influence of color disturbance on the training effect of segmentation network is analyzed, and gave a through study to this problem.

  • Domain-adaptive method was utilized to relieve the scarcity of data caused cross-domain histopathological segmentation problem, and achieved remarkable performance on cross-domain breast cancer segmentation.

  • In order to ensure the data has a highly reliability, an entropy sorting-based data sorting method is proposed.

  • A representative selection method is proposed to select the high representativeness and reference data, which can make our proposed network achieve enough discriminative and robustness.

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References

  1. Achanta SDM, Karthikeyan T (2019) A wireless IOT system towards gait detection technique using FSR sensor and wearable IOT devices. Int J Intell Unmanned Syst 8(1):43–54

    Article  Google Scholar 

  2. Achanta SDM, Karthikeyan T, Vinothkanna R (2019) A novel hidden Markov model-based adaptive dynamic time warping (HMDTW) gait analysis for identifying physically challenged persons. Soft Comput 23(18):8359–8366

    Article  Google Scholar 

  3. Bayramoglu N, Kannala J, Heikkilä J (2016) Deep learning for magnification independent breast cancer histopathology image classification. In: Proceeding of the International Conference on Pattern Recognition, Cancun, Mexico, pp 2440–2445

  4. Beeravolu AR, Azam S, Jonkman M, Shanmugam B, Kannoorpatti K, Anwar A (2021) Preprocessing of breast Cancer images to create datasets for deep-CNN. IEEE Access 9:33438–33463

    Article  Google Scholar 

  5. Boykov YY, Jolly MP (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in ND images. In: Proceeding of International Conference on Pattern Recognition, Vancouver, British Columbia, Canada, pp 105–112

  6. Buslaev A, Parinov A, Khvedchenya E, Iglovikov VI, Kalinin A. A. (2018) Albumentations: fast and flexible image augmentations. arXiv, arXiv:1809.06839.

  7. Chaurasia A, Culurciello E (2017) LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation. IEEE Visual Communications and Image Processing, St. Petersburg, FL, USA, pp 1–4

  8. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional Cets, Atrous convolution, and fully connected Crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

    Article  Google Scholar 

  9. Courty N, Flamary R, Tuia D, Rakotomamonjy A (2017) Optimal transport for domain adaptation. IEEE Trans Pattern Anal Mach Intell 39(9):1853–1865

    Article  Google Scholar 

  10. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. (2009) Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Sparkle East, USA, pp 248–255

  11. Dice LR (1945) Measures of the amount of ecologic association between species. ECY Ecol 26(3):297–302

    Article  Google Scholar 

  12. Dou Q, Ouyang C, Chen C, Chen H, Heng P-A (n.d.) Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss. arXiv2018, arXiv:1804.10916

  13. Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C (2016) The importance of skip connections in biomedical image segmentation. In: Deep Learning and Data Labeling for Medical Applications, Springer: Cham, pp. 179–187

  14. Feige U (1998) A threshold of ln n for approximating set cover. J ACM 45(4):634–652

    Article  MathSciNet  Google Scholar 

  15. Foran DJ, Yang L, Tuzel O, Chen W, Hu J, Kurc TM, Ferreira R, Saltz JH (2009) A Cagrid-Enabled Learning based Image Segmentation Method for Histopathology Specimens. In: Proceeding of the International Symposium on Biomedical Imaging, Boston, USA, pp 1306–1309

  16. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, pp. 2672–2680

  17. 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, Las Vegas, USA, pp. 770–778

  18. Kamnitsas K, Baumgartner C, Ledig C, Newcombe V, Simpson J, Kane A, Menon D, Nori A, Criminisi A, Rueckert D, Glocker B (2017) Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. Proceedings of the International Conference on Information Processing in Medical Imaging, Boone, NC, USA, pp. 597–609

  19. Khaki S, Pham H, Han Y, Kuhl A, Kent W, Wang L (2020) Convolutional neural networks for image-based corn kernel detection and counting. Sensors 20:2721

    Article  Google Scholar 

  20. Kong J, Shimada H, Boyer K, Saltz J, Gurcan M (2007) Image analysis for automated assessment of grade of neuroblastic differentiation. In: Proceeding of the International Symposium on Biomedical Imaging, Washington, USA, pp. 61–64

  21. Kong H, Gurcan M, Belkacem-Boussaid K (2011) Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imaging 30(9):1661–1677

    Article  Google Scholar 

  22. Lahoura V, Singh H, Aggarwal A, Sharma B, Mohammed MA, Damaševičius R, Cengiz K (2021) Cloud computing-based framework for breast Cancer diagnosis using extreme learning machine. Diagnostics 11(2):241

    Article  Google Scholar 

  23. Lee DH (2013) Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. Workshop on Challenges in Representation Learning, ICML 3(2)

  24. Leng L, Yang Z, Kim C, Zhang Y (2020) A light-weight practical framework for feces detection and trait recognition. Sensors 20:2644

    Article  Google Scholar 

  25. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, pp. 3431–3440

  26. Long M, Zhu H, Wang J, Jordan MI (2016) Unsupervised domain adaptation with residual transfer networks. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain, 136–144

  27. Malebary SJ, Hashmi A (2021) Automated breast mass classification system using deep learning and ensemble learning in digital mammogram. IEEE Access 9:55312–55328

    Article  Google Scholar 

  28. Nagao T, Sato E, Inoue R, Oshiro H, Takahashi RH, Nagai T, Yoshida M, Suzuki F, Obikane H, Yamashina M, Matsubayashi J (2012) Immunohistochemical analysis of salivary gland tumors: application for surgical pathology practice. Acta Histochem Cytochem 45(5):269–282

    Article  Google Scholar 

  29. Nguyen K, Jain AK, Allen RL (2010) Automated Gland Segmentation and Classification for Gleason Grading of Prostate Tissue Images. In Proceeding of International Conference on Pattern Recognition, Istanbul, Turkey, pp. 1497–1500

  30. Qu A, Chen J, Wang L, Yuan J, Yang F, Xiang Q, Maskey N, Yang G, Liu J, Li Y (2015) Segmentation of hematoxylin-eosin stained breast Cancer histopathological images based on pixel-wise SVM classifier. Sci China Inf Sci 58:1–13

    Article  Google Scholar 

  31. Roccetti M, Delnevo G, Casini L, Mirri S (2021) An alternative approach to dimension reduction for Pareto distributed data: a case study. J Big Data 8(1):1–23

    Article  Google Scholar 

  32. Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. Comput Vis Pattern Recognit arXiv:1505.04597

  33. Ruiz A, Kong J, Ujaldon M, Boyer K, Saltz J, Gur-Can M (2008) Pathological image segmentation for neuroblastoma using the GPU. In: Proceeding of the International Symposium on Biomedical Imaging, Paris, France, pp. 296–299

  34. Saber A, Sakr M, Abo-Seida OM, Keshk A, Chen H (2021) A novel deep-learning model for automatic detection and classification of breast Cancer using the transfer-learning technique. IEEE Access 9:71194–71209

    Article  Google Scholar 

  35. Shen R, Yan K, Tian K, Jiang C, Zhou K (2019) Breast mass detection from the digitized x-ray mammograms based on the combination of deep active learning and self-paced learning. Futur Gener Comput Syst 101:668–679

    Article  Google Scholar 

  36. Shen H, Tian K, Dong P, Zhang J, Yan K, Che S, Yao J, Luo P, Han X (2020) Deep Active Learning for Breast Cancer Segmentation on Immunohistochemistry Images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 509–518

  37. Somasundaram A, Reddy US (2016) Data imbalance: Effects and Solutions for Classification of Large and Highly Imbalanced Data. In International Conference on Research in Engineering, Computers and Technology (ICRECT), Tiruchirappalli, India, pp 1–16

  38. Sun B, Saenko K (2016) Deep coral: correlation alignment for deep domain adaptation. Proceedings of the European Conference on Computer Vision, Amsterdam Netherlands, pp. 443–450

  39. Sun Y, Wong AK, Kamel MS (2009) Classification of imbalanced data: a review. Int J Pattern Recognit Artif Intell 23(04):687–719

    Article  Google Scholar 

  40. Taher F, Werghi N, Al-Ahmad H, Donner C (2013) Extraction and segmentation of sputum cells for lung Cancer early diagnosis. Algorithms 6:512–531

    Article  Google Scholar 

  41. Taher F, Werghi N, Al-Ahmad H (2015) Computer aided diagnosis system for early lung Cancer detection. Algorithms 8:1088–1110

    Article  MathSciNet  Google Scholar 

  42. Tahmoush D (2009) Image similarity to improve the classification of breast Cancer images. Algorithms 2:1503–1525

    Article  Google Scholar 

  43. Theriot CM, Joshua RF (2019) Human fecal Metabolomic profiling could inform Clostridioides difficile infection diagnosis and treatment. J Clin Invest 129:3539–3541

    Article  Google Scholar 

  44. Tsai YH, Hung WC, Schulter S, Sohn K, Yang MH, Chandraker M (2018) Learning to adapt structured output space for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, pp 7472–7481

  45. Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T (2014) Deep domain confusion: Maximizing for domain invariance. arXiv:1412.3474

  46. Van Opbroek A, Achterberg HC, Vernooij MW, De Bruijne M (2019) Transfer learning for image segmentation by combining image weighting and kernel learning. IEEE Trans Med Imaging:213–224

  47. Vese LA, Chan TF (2002) A multiphase level set framework for image segmentation using the Mumford and Shah model. Int J Comput Vis 50:271–293

    Article  Google Scholar 

  48. Vu TH, Jain H, Bucher M, Cord M, Pérez P (2019) Advent: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, California, USA, pp. 2517–2526

  49. Vununu C, Lee S-H, Kwon K-R (2020) A strictly unsupervised deep learning method for HEp-2 cell image classification. Sensors 20:2717

    Article  Google Scholar 

  50. Wilkowski A, Stefańczyk M, Kasprzak W (2020) Training data extraction and object detection in surveillance scenario. Sensors 20:2689

    Article  Google Scholar 

  51. Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Quebec, Canada, pp 399–407

  52. Yang Z, Leng L, Kim B-G (2019) StoolNet for color classification of stool medical images. Electronics 8:1464

    Article  Google Scholar 

  53. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp 2881–2890

  54. Zheng Y (2010) Breast Cancer detection with Gabor features from digital mammograms. Algorithms 3:44–62

    Article  Google Scholar 

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Acknowledgements

This research was funded by National Natural Science Foundation of China, grant number 62066027.

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Authors and Affiliations

  1. School of Information Engineering, Nanchang University, Nanchang, 330031, People’s Republic of China

    Zhenrong Lin, Jidong Li, Qipeng Yao & Lihang Wan

  2. AI Lab, Tencent, Shenzhen, 518000, People’s Republic of China

    Haocheng Shen

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  1. Zhenrong Lin
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Correspondence to Qipeng Yao.

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Lin, Z., Li, J., Yao, Q. et al. Adversarial learning with data selection for cross-domain histopathological breast Cancer segmentation. Multimed Tools Appl 81, 5989–6008 (2022). https://doi.org/10.1007/s11042-021-11814-y

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