Abstract
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. Several approaches have been recently considered to encode attributes in the latent space to enhance its interpretability. Notably, attribute regularization aims to encode a set of attributes along the dimensions of a latent representation. However, this approach is based on Variational AutoEncoder and suffers from blurry reconstruction. In this paper, we propose an Attributed-regularized Soft Introspective Variational Autoencoder that combines attribute regularization of the latent space within the framework of an adversarially trained variational autoencoder. We demonstrate on short-axis cardiac Magnetic Resonance images of the UK Biobank the ability of the proposed method to address blurry reconstruction issues of variational autoencoder methods while preserving the latent space interpretability.
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References
Bai, W., Sinclair, M., Tarroni, G., et al.: Automated cardiovascular magnetic resonance image analysis with fully convolutional networks. J. Cardiovasc. Magn. Reson. 20(1), 1–12 (2018)
Biffi, C., Cerrolaza, J.J., Tarroni, G., et al.: Explainable anatomical shape analysis through deep hierarchical generative models. IEEE Trans. Med. Imaging 39(6), 2088–2099 (2020)
Cetin, I., Stephens, M., Camara, O., et al.: Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders. Comput. Med. Imaging Graph. 104, 102158 (2023)
Daniel, T., Tamar, A.: Soft-introVAE: analyzing and improving the introspective variational autoencoder. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021)
Engel, J., Hoffman, M., Roberts, A.: Latent constraints: learning to generate conditionally from unconditional generative models. arXiv:1711.05772 (2017)
Ghosh, P., Sajjadi, M.S., Vergari, A., Black, M., Schölkopf, B.: From variational to deterministic autoencoders. arXiv preprint arXiv:1903.12436 (2019)
Hager, P., Menten, M.J., Rueckert, D.: Best of both worlds: multimodal contrastive learning with tabular and imaging data. In: Conference on Computer Vision and Pattern Recognition (2023)
Higgins, I., Matthey, L., Pal, A., Burgess, C., et al.: beta-VAE: learning basic visual concepts with a constrained variational framework. In: International Conference on Learning Representations (2017)
Huang, H., He, R., Sun, Z., Tan, T., et al.: IntroVAE: introspective variational autoencoders for photographic image synthesis. Adv. Neural Inf. Process. Syst. 31 (2018)
Kalatzis, D., Eklund, D., Arvanitidis, G., Hauberg, S.: Variational autoencoders with riemannian brownian motion priors. arXiv preprint arXiv:2002.05227 (2020)
Lample, G., Zeghidour, N., Usunier, N., et al.: Fader networks: manipulating images by sliding attributes. Adv. Neural Inf. Process. Syst. 30 (2017)
Liu, W., Li, R., Zheng, M., et al.: Towards visually explaining variational autoencoders. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Pati, A., Lerch, A.: Attribute-based regularization of latent spaces for variational auto-encoders. Neural Comput. Appl. 33, 4429–4444 (2021)
Petersen, S.E., Matthews, P.M., Francis, J.M., et al.: UK biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18(1), 8 (2016)
Pidhorskyi, S., Almohsen, R., Doretto, G.: Generative probabilistic novelty detection with adversarial autoencoders. Adv. Neural Inf. Process. Syst. 31 (2018)
Razavi, A., Van den Oord, A., Vinyals, O.: Generating diverse high-fidelity images with VQ-VAE-2. Adv. Neural Inf. Process. Syst. 32 (2019)
Ridgeway, K., Mozer, M.C.: Learning deep disentangled embeddings with the f-statistic loss. Adv. Neural Inf. Process. Syst. 31 (2018)
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)
Sønderby, C.K., Raiko, T., Maaløe, L., Sønderby, S.K., Winther, O.: Ladder variational autoencoders. Adv. Neural Inf. Process. Syst. 29 (2016)
Vahdat, A., Kautz, J.: NVAE: a deep hierarchical variational autoencoder. Adv. Neural. Inf. Process. Syst. 33, 19667–19679 (2020)
Xu, H., Luo, D., Henao, R., Shah, S., Carin, L.: Learning autoencoders with relational regularization. In: International Conference on Machine Learning, pp. 10576–10586. PMLR (2020)
Zhang, R., Isola, P., Efros, A.A., et al.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
This research has been conducted using the UK Biobank Resource under Application Number 87065 and was supported by the German Federal Ministry of Health on the basis of a decision by the German Bundestag, under the frame of ERA PerMed. C.I.B. is in part supported by the Helmholtz Association under the joint research school “Munich School for Data Science - MUDS”.
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Di Folco, M., Bercea, C.I., Chan, E., Schnabel, J.A. (2024). Interpretable Representation Learning of Cardiac MRI via Attribute Regularization. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15010. Springer, Cham. https://doi.org/10.1007/978-3-031-72117-5_46
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