Abstract
Convolutional neural network based systems have largely failed to be adopted in many high-risk application areas, including healthcare, military, security, transportation, finance, and legal, due to their highly uninterpretable “black-box” nature. Towards solving this deficiency, we teach a novel multi-task capsule network to improve the explainability of predictions by embodying the same high-level language used by human-experts. Our explainable capsule network, X-Caps, encodes high-level visual object attributes within the vectors of its capsules, then forms predictions based solely on these human-interpretable features. To encode attributes, X-Caps utilizes a new routing sigmoid function to independently route information from child capsules to parents. Further, to provide radiologists with an estimate of model confidence, we train our network on a distribution of expert labels, modeling inter-observer agreement and punishing over/under confidence during training, supervised by human-experts’ agreement. X-Caps simultaneously learns attribute and malignancy scores from a multi-center dataset of over 1000 CT scans of lung cancer screening patients. We demonstrate a simple 2D capsule network can outperform a state-of-the-art deep dense dual-path 3D CNN at capturing visually-interpretable high-level attributes and malignancy prediction, while providing malignancy prediction scores approaching that of non-explainable 3D CNNs. To the best of our knowledge, this is the first study to investigate capsule networks for making predictions based on radiologist-level interpretable attributes and its applications to medical image diagnosis. Code is publicly available at https://github.com/lalonderodney/X-Caps.
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References
Afshar, P., Mohammadi, A., Plataniotis, K.N.: Brain tumor type classification via capsule networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3129–3133. IEEE (2018)
Armato III, S., et al.: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)
Bloomberg, J.: Don’t Trust Artificial Intelligence? Time To Open The AI ‘Black Box’, (11162018), Forbes Magazine. http://www.forbes.com/sites/jasonbloomberg/2018/09/16/dont-trust-artificial-intelligence-time-to-open-the-ai-black-box/#6ceaf3793b4a
Bologna, G.: A model for single and multiple knowledge based networks. Artif. Intell. Med. 28(2), 141–163 (2003)
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172–2180 (2016)
Frankle, J., Carbin, M.: The lottery ticket hypothesis: finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635 (2018)
Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89. IEEE (2018)
Hancock, M., Magnan, J.: Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms. J. Med. Imaging 3(4), 044504 (2016)
Hussein, S., Cao, K., Song, Q., Bagci, U.: Risk stratification of lung nodules using 3D CNN-based multi-task learning. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 249–260. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_20
Hussein, S., Gillies, R., Cao, K., Song, Q., Bagci, U.: Tumornet: lung nodule characterization using multi-view convolutional neural network with gaussian process. In: 14th International Symposium on Biomedical Imaging (ISBI), pp. 1007–1010. IEEE (2017)
Iesmantas, T., Alzbutas, R.: Convolutional capsule network for classification of breast cancer histology images. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 853–860. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_97
Jiménez-Sánchez, A., Albarqouni, S., Mateus, D.: Capsule networks against medical imaging data challenges. In: Stoyanov, D., et al. (eds.) LABELS/CVII/STENT -2018. LNCS, vol. 11043, pp. 150–160. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01364-6_17
Kandel, P., LaLonde, R., Ciofoaia, V., Wallace, M.B., Bagci, U.: Su1741 colorectal polyp diagnosis with contemporary artificial intelligence. Gastrointest. Endosc. 89(6), AB403 (2019)
Kuhn, M., Johnson, K.: Applied Predictive Modeling, vol. 26. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-6849-3
LaLonde, R., Bagci, U.: Capsules for object segmentation. arXiv preprint arXiv:1804.04241 (2018)
LaLonde, R., Kandel, P., Spampinato, C., Wallace, M.B., Bagci, U.: Diagnosing colorectal polyps in the wild with capsule networks. In: 17th International Symposium on Biomedical Imaging (ISBI). IEEE (2020)
LaLonde, R., Xu, Z., Jain, S., Bagci, U.: Capsules for biomedical image segmentation. arXiv preprint arXiv:2004.04736 (2020)
Lehnis, M.: Can We Trust AI If We Don’t Know How It Works? (15062018), BBC News. http://www.bbc.com/news/business-44466213
Mobiny, A., Lu, H., Nguyen, H.V., Roysam, B., Varadarajan, N.: Automated classification of apoptosis in phase contrast microscopy using capsule network. IEEE Trans. Med. Imaging 39(1), 1–10 (2019)
Mobiny, A., Van Nguyen, H.: Fast CapsNet for lung cancer screening. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 741–749. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_82
Nibali, A., He, Z., Wollersheim, D.: Pulmonary nodule classification with deep residual networks. Int. J. Comput. Assist. Radiol. Surgery 1–10 (2017). https://doi.org/10.1007/s11548-017-1605-6
Pal, A., Chaturvedi, A., Garain, U., Chandra, A., Chatterjee, R., Senapati, S.: CapsDeMM: capsule network for detection of munro’s microabscess in skin biopsy images. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 389–397. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_44
Polonski, V.: People Don’t Trust AI-Here’s How We Can Change That, (10012018), Scientific American. http://www.scientificamerican.com/article/people-dont-trust-ai-heres-how-we-can-change-that/
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3856–3866 (2017)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Shen, S., Han, S.X., Aberle, D.R., Bui, A.A., Hsu, W.: An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert Syst. Appl. 128, 84–95 (2019)
Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J.: Multi-scale convolutional neural networks for lung nodule classification. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 588–599. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19992-4_46
Shen, W., et al.: Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn. 61, 663–673 (2017)
Shen, Y., Gao, M.: Dynamic routing on deep neural network for thoracic disease classification and sensitive area localization. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 389–397. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_45
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
Zhu, W., Liu, C., Fan, W., Xie, X.: Deeplung: deep 3D dual path nets for automated pulmonary nodule detection and classification. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 673–681. IEEE (2018)
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This project is partially supported by the NIH funding: R01-CA246704 and R01-CA240639.
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LaLonde, R., Torigian, D., Bagci, U. (2020). Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_29
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