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HR-CAM: Precise Localization of Pathology Using Multi-level Learning in CNNs

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11767)


We propose a CNN based technique that aggregates feature maps from its multiple layers that can localize abnormalities with greater details as well as predict pathology under consideration. Existing class activation mapping (CAM) techniques extract feature maps from either the final layer or a single intermediate layer to create the discriminative maps and then interpolate to upsample to the original image resolution. In this case, the subject specific localization is coarse and is unable to capture subtle abnormalities. To mitigate this, our method builds a novel CNN based discriminative localization model that we call high resolution CAM (HR-CAM), which accounts for layers from each resolution, therefore facilitating a comprehensive map that can delineate the pathology for each subject by combining low-level, intermediate as well as high-level features from the CNN. Moreover, our model directly provides the discriminative map in the resolution of the original image facilitating finer delineation of abnormalities. We demonstrate the working of our model on a simulated abnormalities data where we illustrate how the model captures finer details in the final discriminative maps as compared to current techniques. We then apply this technique: (1) to classify ependymomas from grade IV glioblastoma on T1-weighted contrast enhanced (T1-CE) MRI and (2) to predict Parkinson’s disease from neuromelanin sensitive MRI. In all these cases we demonstrate that our model not only predicts pathologies with high accuracies, but also creates clinically interpretable subject specific high resolution discriminative localizations. Overall, the technique can be generalized to any CNN and carries high relevance in a clinical setting.


  • Class Activation Map (CAM)
  • Convolutional Neural Networks (CNN)
  • High resolution
  • Ependymoma
  • Gliobastoma
  • Parkinson’s disease

S. Shinde and T. Chougule—Equally contributed.

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  • DOI: 10.1007/978-3-030-32251-9_33
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  1. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)

    Google Scholar 

  2. Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    CrossRef  Google Scholar 

  3. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014).

    CrossRef  Google Scholar 

  4. Zhou, B., et al.: Learning deep features for discriminative localization. In: CVPR (2016)

    Google Scholar 

  5. Selvaraju, R.R., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    Google Scholar 

  6. Zhao, G., Zhou, B., Wang, K., Jiang, R., Xu, M.: Respond-CAM: analyzing deep models for 3D imaging data by visualizations. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 485–492. Springer, Cham (2018).

    CrossRef  Google Scholar 

  7. Ahmad, A., et al.: Predictive and discriminative localization of IDH genotype in high grade gliomas using deep convolutional neural nets. In: IEEE 16th International Symposium on Biomedical Imaging (2019)

    Google Scholar 

  8. Chattopadhay, A., et al.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2018)

    Google Scholar 

  9. Rosenfeld, A., Ullman, S.: Visual concept recognition and localization via iterative introspection. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10115, pp. 264–279. Springer, Cham (2017).

    CrossRef  Google Scholar 

  10. He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, pp. 770–778 (2016)

    Google Scholar 

  11. Shinde, S., et al.: Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage Clin. 22, 101748 (2019)

    CrossRef  Google Scholar 

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Correspondence to Madhura Ingalhalikar .

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Shinde, S., Chougule, T., Saini, J., Ingalhalikar, M. (2019). HR-CAM: Precise Localization of Pathology Using Multi-level Learning in CNNs. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham.

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