Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks (original) (raw)

References

  1. Bain, B. J. Diagnosis from the blood smear. N. Engl. J. Med.353, 498–507 (2005).
    Article Google Scholar
  2. Tkachuk, D. C. & Hirschmann, J. V. Wintrobe’s Atlas of Clinical Hematology (Lippincott Raven, 2006).
  3. Theml, H., Diem, H. & Haferlach, T. Color Atlas of Hematology (Thieme, 2004).
  4. Döhner, H. et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood129, 424–447 (2017).
    Article Google Scholar
  5. Swerdlow, S. H. et al. (eds) WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues 4th edn (International Agency for Research on Cancer, 2017).
  6. Arber, D. A. et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood127, 2391–2405 (2016).
    Article Google Scholar
  7. Bennett, J. M. et al. Proposed revised criteria for the classification of acute myeloid leukemia. A report of the French–American–British Cooperative Group. Ann. Intern. Med.103, 620–625 (1985).
    Article Google Scholar
  8. Font, P. et al. Inter-observer variance with the diagnosis of myelodysplastic syndromes (MDS) following the 2008 WHO classification. Ann. Hematol.92, 19–24 (2013).
    Article Google Scholar
  9. Font, P. et al. Interobserver variance in myelodysplastic syndromes with less than 5% bone marrow blasts: unilineage vs. multilineage dysplasia and reproducibility of the threshold of 2% blasts. Ann. Hematol.94, 565–573 (2015).
    Article Google Scholar
  10. Fuentes-Arderiu, X. & Dot-Bach, D. Measurement uncertainty in manual differential leukocyte counting. Clin. Chem. Lab. Med.47, 112–115 (2009).
    Article Google Scholar
  11. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).
  12. Rawat, W. & Wang, Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput.29, 2352–2449 (2017).
    Article MathSciNet MATH Google Scholar
  13. Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Int. J. Comput. Vision115, 211–252 (2015).
    Article MathSciNet Google Scholar
  14. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature542, 115–118 (2017).
    Article Google Scholar
  15. Eulenberg, P. et al. Reconstructing cell cycle and disease progression using deep learning. Nat. Commun.8, 463 (2017).
    Article Google Scholar
  16. Janowczyk, A. & Madabhushi, A. Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform.7, 29 (2016).
    Article Google Scholar
  17. Fuchs, T. J. & Buhmann, J. M. Computational pathology: challenges and promises for tissue analysis. Comput. Med. Imaging Graph.35, 515–530 (2011).
    Article Google Scholar
  18. Albarqouni, S. et al. AggNet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans. Med. Imaging35, 1313–1321 (2016).
    Article Google Scholar
  19. Levenson, R. M., Fornari, A. & Loda, M. Multispectral imaging and pathology: seeing and doing more. Expert Opin. Med. Diagn.2, 1067–1081 (2008).
    Article Google Scholar
  20. Gertych, A. et al. Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph.46, 197–208 (2015).
    Article Google Scholar
  21. Bigorra, L., Merino, A., Alférez, S. & Rodellar, J. Feature analysis and automatic identification of leukemic lineage blast cells and reactive lymphoid cells from peripheral blood cell images. J. Clin. Lab. Anal.31, e22024 (2017).
    Article Google Scholar
  22. Krappe, S., Wittenberg, T., Haferlach, T. & Munzenmayer, C. Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus–plasma separation and cell classification using a hierarchical tree model of hematopoesis. Proc. SPIE9785, 97853C (2016).
    Google Scholar
  23. Scotti, F. Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. In Computational Intelligence for Measurement Systems and Applications (CIMSA) 96–101 (IEEE, 2005).
  24. Mohapatra, S., Patra, D. & Satpathy, S. An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Comput. Appl.24, 1887–1904 (2014).
    Article Google Scholar
  25. Greenspan, H., van Ginneken, B. & Summers, R. M. Deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans. Med. Imaging35, 1153–1159 (2016).
    Article Google Scholar
  26. Shen, D., Wu, G. & Suk, H. Deep learning in medical image analysis. Ann. Rev. Biomed. Eng.19, 221–248 (2017).
    Article Google Scholar
  27. Choi, J. W. et al. White blood cell differential count of maturation stages in bone marrow smear using dual-stage convolutional neural networks. PLoS One12, e0189259 (2017).
    Article Google Scholar
  28. Kainz, P., Burgsteiner, H., Asslaber, M. & Ahammer, H. Training echo state networks for rotation-invariant bone marrow cell classification. Neural Comput. Appl.28, 1277–1292 (2017).
    Article Google Scholar
  29. Su, M.-C., Cheng, C.-Y. & Wang, P.-C. A neural-network-based approach to white blood cell classification. Sci. World J.2014, 796371 (2014).
    Google Scholar
  30. Macawile, M. J., Quiñones, V. V., Ballado, A., Cruz, J. D. & Caya, M. V. White blood cell classification and counting using convolutional neural network. In 2018 3rd International Conference on Control and Robotics Engineering (ICCRE) 259–263 (IEEE, 2018).
  31. Keohane, E. M., Smith, L. & Walenga, J. M. Rodak’s Hematology—Clinical Principles and Applications 5th edn (Elsevier, 2016).
  32. Xie, S., Girshick, R., Dollár, P., Tu, Z. & He, K. Aggregated residual transformations for deep neural networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 5987–5995 (IEEE, 2017).
  33. Dietz, M. ResNeXt implementation for Keras. GitHub Gist https://gist.githubusercontent.com/mjdietzx/ (2017).
  34. Chollet, F. et al. Keras 2.0. Keras https://keras.io (2017).
  35. Bychkov, D. et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep.8, 3395 (2018).
    Article Google Scholar
  36. Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. Preprint at https://arxiv.org/abs/1312.6034 (2013).
  37. Mandrekar, J. N. Receiver operating characteristic curve in diagnostic test assessment. J. Thorac. Oncol.5, 1315–1316 (2010).
    Article Google Scholar
  38. Hosmer, D. & Lemeshow, S. Applied Logistic Regression 2nd edn (Wiley, 2000).
  39. Xing, F. & Yang, L. Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE Rev. Biomed. Eng.9, 234–263 (2016).
    Article Google Scholar
  40. Cuevas, E. et al. White blood cell segmentation by circle detection using electromagnetism-like optimization. Comput. Math. Methods Med.2013, 395071 (2013).
    MathSciNet Google Scholar
  41. Alomari, Y. M., Abdullah, S. N. H. S., Azma, R. Z. & Omar, K. Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm. Comput. Math. Methods Med.2014, 979302 (2014).
    Article MATH Google Scholar
  42. He, K., Gkioxari, G., Dollár, P. & Girshick, R. Mask R-CNN. In Proceedings of the International Conference on Computer Vision (ICCV) 2980–2988 (IEEE, 2017).
  43. Matek, C., Schwarz, S., Spiekermann, K. & Marr, C. A single-cell morphological dataset of leukocytes from AML patients and non-malignant controls (AML-Cytomorphology_LMU). TCAI https://doi.org/10.7937/tcia.2019.36f5o9ld (2019).
  44. Matek, C., Schwarz, S., Spiekermann, K. & Marr, C. A neural network for classifying leukocyte images from blood smears. CodeOcean https://codeocean.com/capsule/9068249/tree/v1 (2019).

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