Abstract
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.
S. Shinde and T. Chougule—Equally contributed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)
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)
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). https://doi.org/10.1007/978-3-319-10590-1_53
Zhou, B., et al.: Learning deep features for discriminative localization. In: CVPR (2016)
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)
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). https://doi.org/10.1007/978-3-030-00928-1_55
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)
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)
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). https://doi.org/10.1007/978-3-319-54193-8_17
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)
Shinde, S., et al.: Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage Clin. 22, 101748 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Shinde, S., Chougule, T., Saini, J., Ingalhalikar, M. (2019). HR-CAM: Precise Localization of Pathology Using Multi-level Learning in CNNs. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-32251-9_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32250-2
Online ISBN: 978-3-030-32251-9
eBook Packages: Computer ScienceComputer Science (R0)