Abstract
Acute leukemia is a malignant clonal disease of hematopoietic stem cells, which is usually diagnosed by morphological examination of bone marrow cells. However, the morphological examination usually relies on the subjective inference of cell morphology experts and is labor-intensive. With the development of computer vision, automatic classification and counting of blood cells is increasingly popular, which greatly improves work efficiency. Within this context, we here propose a novel method for neutrophil classification, which is based on deep neural network. In brief, it first crops the single cells from the large images, and then makes use of the loss functions designed for face recognition and weakly-supervised fine-grained visual classification. With the hybrid loss, the trained network can focus on nucleus areas, extract features with inter-class differences and intra-class compactness. Experiments show that the proposed method can obtain higher overall accuracy. Data is available at https://github.com/stevenxmy/subAML-dataset.git.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bennett, J.M., et al.: Proposals for the classification of the acute leukaemias French-American-British (fab) co-operative group. Br. J. Haematol. 33(4), 451–458 (1976)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Chang, D., et al.: The devil is in the channels: mutual-channel loss for fine-grained image classification. IEEE Trans. Image Process. 29, 4683–4695 (2020)
Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Jin, H., et al.: Developing and preliminary validating an automatic cell classification system for bone marrow smears: a pilot study. J. Med. Syst. 44(10), 1–10 (2020)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, L., et al.: Deep learning for generic object detection: a survey. Int. J. Comput. Vis. 128(2), 261–318 (2020)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)
Matek, C., Schwarz, S., Spiekermann, K., Marr, C.: Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks. Nat. Mach. Intell. 1(11), 538–544 (2019)
Qiu, C., Zhou, W.: A survey of recent advances in CNN-based fine-grained visual categorization. In: 2020 IEEE 20th International Conference on Communication Technology (ICCT), pp. 1377–1384. IEEE (2020)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks (2016)
Sarrafzadeh, O., Rabbani, H., Talebi, A., Banaem, H.U.: Selection of the best features for leukocytes classification in blood smear microscopic images. In: Medical Imaging 2014: Digital Pathology, vol. 9041, p. 90410P. International Society for Optics and Photonics (2014)
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265–5274 (2018)
Wei, X.S., Xie, C.W., Wu, J., Shen, C.: Mask-CNN: localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recogn. 76, 704–714 (2018)
Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Acknowledgements
We thank Christian Matek and Antje for the morphologcial dataset of leukocytes.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, Q., Lu, D., Zhang, T., Yin, J., Yang, J. (2021). Fine-Grained Classification of Neutrophils with Hybrid Loss. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-87355-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87354-7
Online ISBN: 978-3-030-87355-4
eBook Packages: Computer ScienceComputer Science (R0)