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An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets


Owing to improvements in image recognition via deep learning, machine-learning algorithms could eventually be applied to automated medical diagnoses that can guide clinical decision-making. However, these algorithms remain a ‘black box’ in terms of how they generate the predictions from the input data. Also, high-performance deep learning requires large, high-quality training datasets. Here, we report the development of an understandable deep-learning system that detects acute intracranial haemorrhage (ICH) and classifies five ICH subtypes from unenhanced head computed-tomography scans. By using a dataset of only 904 cases for algorithm training, the system achieved a performance similar to that of expert radiologists in two independent test datasets containing 200 cases (sensitivity of 98% and specificity of 95%) and 196 cases (sensitivity of 92% and specificity of 95%). The system includes an attention map and a prediction basis retrieved from training data to enhance explainability, and an iterative process that mimics the workflow of radiologists. Our approach to algorithm development can facilitate the development of deep-learning systems for a variety of clinical applications and accelerate their adoption into clinical practice.

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Fig. 1: System overview.
Fig. 2: Summary of the system outputs.
Fig. 3: Iterative performance improvement by network optimization and preprocessing.
Fig. 4: Test performance for ICH detection.
Fig. 5: Examples of ICH atlas and prediction basis.

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Data availability

The training, validation and test datasets generated for this study are protected patient information. Some data may be available for research purposes from the corresponding author upon reasonable request.


  1. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    Article  CAS  Google Scholar 

  2. Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).

    Article  Google Scholar 

  3. Rajpurkar, P. et al. Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning. Preprint at (2017).

  4. Castelvecchi, D. Can we open the black box of AI? Nature 538, 20–23 (2016).

    Article  CAS  Google Scholar 

  5. Clinical and Patient Decision Support Software. Draft Guidance for Industry and Food and Drug Administration Staff (US FDA, 2017).

  6. Deng, J. et al. Imagenet: a large-scale hierarchical image database. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).

  7. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at (2014).

  8. He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016).

  9. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2818–2826 (IEEE, 2016).

  10. Szegedy, C., Ioffe, S., Vanhoucke, V. & Alemi, A. A. Inception-v4, inception-ResNet and the impact of residual connections on learning. In Proc. 31st AAAI Conference on Artificial Intelligence 4278–4284 (AAAI, 2017).

  11. Wang, X. et al. ChestX-ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 3462–3471 (IEEE, 2017).

  12. Sozykin, K., Khan, A. M., Protasov, S. & Hussain, R. Multi-label class-imbalanced action recognition in hockey videos via 3D convolutional neural networks. Preprint at (2017).

  13. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. Learning deep features for discriminative localization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2921–2929 (IEEE, 2016).

  14. Selvaraju, R. R. et al. Grad-cam: visual explanations from deep networks via gradient-based localization. Preprint at (2016).

  15. Lazer, D., Kennedy, R., King, G. & Vespignani, A. The parable of Google Flu: traps in big data analysis. Science 343, 1203–1205 (2014).

    Article  CAS  Google Scholar 

  16. Chilamkurthy, S. et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet (2018).

  17. Grewal, M., Srivastava, M. M., Kumar, P. & Varadarajan, S. RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In IEEE International Symposium on Biomedical Imaging 281–284 (IEEE, 2018).

  18. Desai, V., Flanders, A. E. & Lakhani, P. Application of deep learning in neuroradiology: automated detection of basal ganglia hemorrhage using 2D-convolutional neural networks. Preprint at (2017).

  19. Phong, T. D. et al. Brain hemorrhage diagnosis by using deep learning. In Proc. 2017 International Conference on Machine Learning and Soft Computing 34–39 (ACM, 2017).

  20. Prevedello, L. M. et al. Automated critical test Findings identification and online notification system using artificial ntelligence in imaging. Radiology 285, 923–931 (2017).

    Article  Google Scholar 

  21. Arbabshirani, M. R. et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digit. Med. 1, 9 (2018).

    Article  Google Scholar 

  22. Rubin, J. et al. Large scale automated reading of frontal and lateral chest X-rays using dual convolutional neural networks. Preprint at (2018).

  23. Lakhani, P. & Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574–582 (2017).

    Article  Google Scholar 

  24. Domingos, P. A few useful things to know about machine learning. Commun. ACM 55, 78–87 (2012).

    Article  Google Scholar 

  25. Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158 (2018).

    Article  Google Scholar 

  26. Brinjikji, W. et al. Inter- and intraobserver agreement in CT characterization of nonaneurysmal perimesencephalic subarachnoid hemorrhage. AJNR Am. J. Neuroradiol. 31, 1103–1105 (2010).

    Article  CAS  Google Scholar 

  27. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. Object detectors emerge in deep scene cnns. Preprint at (2014).

  28. Yosinski, J., Clune, J., Nguyen, A., Fuchs, T. & Lipson, H. Understanding neural networks through deep visualization. Preprint at (2015).

  29. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436 (2015).

    Article  CAS  Google Scholar 

  30. Tsoumakas, G. & Katakis, I. Multi-label classification: an overview. Int. J. Data Warehousing Mining 3, 1–13 (2007).

    Article  Google Scholar 

  31. Russakovsky, O. et al. Imagenet large scale visual recognition challenge. Int. J. Comp. Vision 115, 211–252 (2015).

    Article  Google Scholar 

  32. Chollet, F. et al. Keras (2015);

  33. Lin, M., Chen, Q. & Yan, S. Network in network. Preprint at (2013).

  34. Nesterov, Y. A method of solving the convex programming problem with convergence rate O(1/k 2). Dokl. Akad. Nauk USSR 269, 543–547 (1983).

    Google Scholar 

  35. Abadi, M. et al. Tensorflow: a system for large-scale machine learning. Proc. 12th USENIX Symposium on Operating Systems Design and Implementation 16, 265–283 (2016).

    Google Scholar 

  36. King, G. & Zeng, L. Logistic regression in rare events data. Political Anal. 9, 137–163 (2001).

    Article  Google Scholar 

  37. Kimpe, T. & Tuytschaever, T. Increasing the number of gray shades in medical display systems—how much is enough? J. Digit. Imaging 20, 422–432 (2007).

    Article  Google Scholar 

  38. Xue, Z., Antani, S., Long, L. R., Demner-Fushman, D. & Thoma, G. R. Window classification of brain CT images in biomedical articles. In AMIA Annual Symposium Proceedings 1023 (American Medical Informatics Association, 2012).

  39. Turner, P. & Holdsworth, G. CT stroke window settings: an unfortunate misleading misnomer? Br. J. Radiol. 84, 1061–1066 (2011).

    Article  CAS  Google Scholar 

  40. Ju, C., Bibaut, A. & van der Laan, A. The relative performance of ensemble methods with deep convolutional neural networks for image classification. J. Appl. Stat. 45, 2800–2818 (2018).

    Article  Google Scholar 

  41. Nair, V. & Hinton, G. E. Rectified linear units improve restricted Boltzmann machines. In Proc. 27th International Conference on Machine Learning 807–814 (ICML, 2010).

  42. Davis, J. & Goadrich, M. The relationship between precision-recall and ROC curves. Proc. 23rd International Conference on Machine Learning 233–240 (ACM, 2006).

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The authors would like to acknowledge NVIDIA for the use of a DevBox and providing feedback and support, which made this work possible. R.G.G. is funded in part by an NIH U01 grant under the grant number 5U01EB025153.

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Authors and Affiliations



H.L., S.Y., M.M., R.G.G., M.H.L. and S.D. initiated and designed the research. H.L., S.Y. and M.K. executed the research. M.M., S.H.T., C.E.G., S.A.E., S.R.P., J.M.R., S.K., R.G.G. and M.H.L. acquired and/or interpreted the data. R.G.G. and M.H.L. supervised the data collection. H.L., S.Y., M.H.L. and S.D. analysed and interpreted the data. H.L. and M.K. developed the algorithms and software tools necessary for the experiments. H.L., S.Y., S.H.T. and M.H.L. wrote the manuscript.

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Correspondence to Synho Do.

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Competing interests

M.H.L. is a consultant of GE Healthcare and Takeda Pharmaceutical Company and receives an institutional research support from Siemens Healthcare. S.R.P. is a consultant of GE Healthcare. S.D. is a consultant of Nulogix and Doai and receives research supports from ZCAI, Tplus and MediBloc. The remaining authors declare no competing interests.

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Lee, H., Yune, S., Mansouri, M. et al. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng 3, 173–182 (2019).

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