International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Interpretability on Deep Retinal Image Understanding Network

  • Manal AlGhamdi (Department of Computer Science and Artificial Intelligence, University of Umm AL-Qura)
  • Received : 2024.10.05
  • Published : 2024.10.30
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Abstract

In the last 10 years, artificial intelligence (AI) has shown more predictive accuracy than humans in many fields. Its promising future founded on its great performance increases people's concern about its black-box mechanism. In many fields, such as medicine, mistakes lacking explanations are hardly accepted. As a result, research on interpretable AI is of great significance. Although much work about interpretable AI methods are common in classification tasks, little has focused on segmentation tasks. In this paper, we explored the interpretability on a Deep Retinal Image Understanding (DRIU) network, which is used to segment the vessels from retinal images. We combine the Grad Class Activation Mapping (Grad-CAM), commonly used in image classification, to generate saliency map, with the segmentation task network. Through the saliency map, we got information about the contribution of each layer in the network during predicting the vessels. Therefore, we adjusted the weights of last convolutional layer manually to prove the accuracy of the saliency map generated by Grad-CAM. According to the result, we found the layer 'upsample2' to be the most important during segmentation, and we improved the mIoU score (an evaluation method) to some extent.

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References

  1. S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Mu ̈ller, and W. Samek. On pixel-wise explanations for non- linear classifier decisions by layer-wise relevance propagation. PloS one, 10(7):e0130140, 2015. 
  2. A. Barredo Arrieta, N. D ́iaz-Rodr ́iguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera. Ex- plainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information Fusion, 58:82 - 115, 2020. 
  3. L. Chen, P. Bentley, and D. Rueckert. Fully automatic acute ischemic lesion segmentation in dwi using convolutional neural networks. NeuroImage: Clinical, 15:633-643, 2017. 
  4. A. Dosovitskiy and T. Brox. Inverting convolutional networks with convolutional networks. arXiv preprint arXiv:1506.02753, 4, 2015. 
  5. M. T. Dzindolet, S. A. Peterson, R. A. Pomranky, L. G. Pierce, and H. P. Beck. The role of trust in automation reliance. International journal of human-computer studies, 58(6):697-718, 2003. 
  6. C. Gan, N. Wang, Y. Yang, D.-Y. Yeung, and A. G. Hauptmann. Devnet: A deep event network for multimedia event detection and evidence recounting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015. 
  7. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. 
  8. A. D. Hoover, V. Kouznetsova, and M. Goldbaum. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3):203-210, 2000. 
  9. C. J. Kelly, A. Karthikesalingam, M. Suleyman, G. Corrado, and D. King. Key challenges for delivering clinical impact with artificial intelligence. BMC medicine, 17(1):195, 2019. 
  10. P.-J. Kindermans, S. Hooker, J. Adebayo, M. Alber, K. T. Schu ̈tt, S. Dahne, D. Erhan, and B. Kim. The (un) reliability of saliency methods. arXiv preprint arXiv:1711.00867, 2017. 
  11. A. Mahendran and A. Vedaldi. Understanding deep image representations by inverting them. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5188-5196, 2015. 
  12. A. Mahendran and A. Vedaldi. Salient deconvolutional networks. In B. Leibe, J. Matas, N. Sebe, and M. Welling, editors, Computer Vision - ECCV 2016, pages 120-135, Cham, 2016. Springer International Publishing. 
  13. K.-K. Maninis, J. Pont-Tuset, P. Arbelaez, and L. Van Gool. Deep retinal image understanding. In S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, pages 140-148, Cham, 2016. Springer International Publishing. 
  14. K.-K. Maninis, J. Pont-Tuset, P. Arbelaez, and L. Van Gool. Deep retinal image understanding. In International conference on medical image computing and computer-assisted intervention, pages 140-148. Springer, 2016. 
  15. M. T. Ribeiro, S. Singh, and C. Guestrin." why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135- 1144, 2016. 
  16. O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241. Springer, 2015. 
  17. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), Oct 2017. 
  18. K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013. 
  19. J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Ried- miller. Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806, 2014. 
  20. J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. Van Ginneken. Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4):501-509, 2004. 
  21. E. Tjoa and C. Guan. A survey on explainable artificial intelligence (xai): towards medical xai. arXiv preprint arXiv:1907.07374, 2019. 
  22. S. Xie and Z. Tu. Holistically-nested edge detection. In Proceedings of the IEEE international conference on computer vision, pages 1395-1403, 2015. 
  23. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, editors, Computer Vision - ECCV 2014, pages 818-833, Cham, 2014. Springer International Publishing. 
  24. B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba. Learning deep features for discriminative localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.