Abstract
Early detection of Barrett’s Esophagus (BE), the only known precursor to Esophageal adenocarcinoma (EAC), is crucial for effectively preventing and treating esophageal cancer. In this work, we investigate the potential of geometric Variational Autoencoders (VAEs) to learn a meaningful latent representation that captures the progression of BE. We show that hyperspherical VAE (\(\mathcal {S}\)-VAE ) and Kendall Shape VAE show improved classification accuracy, reconstruction loss, and generative capacity. Additionally, we present a novel autoencoder architecture that can generate qualitative images without the need for a variational framework while retaining the benefits of an autoencoder, such as improved stability and reconstruction quality.
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References
Arvanitidis, G., Hansen, L.K., Hauberg, S.: Latent space oddity: on the curvature of deep generative models. arXiv preprint arXiv:1710.11379 (2017)
Bachmann, G., Bécigneul, G., Ganea, O.: Constant curvature graph convolutional networks. In: International Conference on Machine Learning, pp. 486–496. PMLR (2020)
Bekkers, E.J., Lafarge, M.W., Veta, M., Eppenhof, K.A.J., Pluim, J.P.W., Duits, R.: Roto-translation covariant convolutional networks for medical image analysis. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 440–448. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_50
Chadebec, C., Mantoux, C., Allassonnière, S.: Geometry-aware hamiltonian variational auto-encoder (2020)
Chen, N., Klushyn, A., Kurle, R., Jiang, X., Bayer, J., Smagt, P.: Metrics for deep generative models. In: International Conference on Artificial Intelligence and Statistics, pp. 1540–1550. PMLR (2018)
Cohen, T., Welling, M.: Group equivariant convolutional networks. In: International Conference on Machine Learning, pp. 2990–2999. PMLR (2016)
Davidson, T.R., Falorsi, L., De Cao, N., Kipf, T., Tomczak, J.M.: Hyperspherical variational auto-encoders. arXiv preprint arXiv:1804.00891 (2018)
Gu, A., Sala, F., Gunel, B., Ré, C.: Learning mixed-curvature representations in product spaces. In: International Conference on Learning Representations (2018)
Hussein, M., et al.: A new artificial intelligence system successfully detects and localises early neoplasia in barrett’s esophagus by using convolutional neural networks. United Eur. Gastroenterol. J. 10(6), 528–537 (2022)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Lafarge, M.W., Pluim, J.P., Veta, M.: Orientation-disentangled unsupervised representation learning for computational pathology. arXiv preprint arXiv:2008.11673 (2020)
Shao, H., Kumar, A., Thomas Fletcher, P.: The riemannian geometry of deep generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 315–323 (2018)
Skopek, O., Ganea, O.E., Bécigneul, G.: Mixed-curvature variational autoencoders. arXiv preprint arXiv:1911.08411 (2019)
de Souza Jr, L.A., et al.: A survey on barrett’s esophagus analysis using machine learning. Comput. Biol. Med. 96, 203–213 (2018)
Tosi, A., Hauberg, S., Vellido, A., Lawrence, N.D.: Metrics for probabilistic geometries. arXiv preprint arXiv:1411.7432 (2014)
Vadgama, S., Tomczak, J.M., Bekkers, E.J.: Kendall shape-vae: learning shapes in a generative framework. In: NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations (2022)
Van der Wel, M., Jansen, M., Vieth, M., Meijer, S.: What makes an expert barret’s histopathologist?, vol. 908, pp. 137–159 (2016)
van der Wel, M.J., Coleman, H.G., Bergman, J.J., Jansen, M., Meijer, S.L.: Histopathologist features predictive of diagnostic concordance at expert level among a large international sample of pathologists diagnosing barret’s dysplasia using digital pathology. Gut 69(5), 811–822 (2020)
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van Veldhuizen, V., Vadgama, S., de Boer, O., Meijer, S., Bekkers, E.J. (2023). Modeling Barrett’s Esophagus Progression Using Geometric Variational Autoencoders. In: Ali, S., van der Sommen, F., van Eijnatten, M., Papież, B.W., Jin, Y., Kolenbrander, I. (eds) Cancer Prevention Through Early Detection. CaPTion 2023. Lecture Notes in Computer Science, vol 14295. Springer, Cham. https://doi.org/10.1007/978-3-031-45350-2_11
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