[1] Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U.R., et al.: A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion (2021)
[2] Arganda-Carreras, I., Turaga, S.C., Berger, D.R., Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J., Laptev, D., Dwivedi, S., Buhmann, J.M., et al.: Crowdsourcing the creation of image segmentation algorithms for connectomics. Frontiers in neuroanatomy (2015)
[3] Banerjee, S., Magee, L., Wang, D., Li, X., Huo, B.X., Jayakumar, J., Matho, K., Lin, M.K., Ram, K., Sivaprakasam, M., et al.: Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks. Nature machine intelligence (2020)
[4] Baumgartner, C.F., Tezcan, K.C., Chaitanya, K., Hotker, A.M., Muehlematter, U.J., Schawkat, K., Becker, A.S., Donati, O., Konukoglu, E.: Phiseg: Capturing uncertainty in medical image segmentation. In: MICCAI (2019)
[5] Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062 (2014)
[6] Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. TPAMI (2017)
[7] Clough, J.R., Byrne, N., Oksuz, I., Zimmer, V.A., Schnabel, J.A., King, A.P.: A topological loss function for deep-learning based image segmentation using persistent homology. TPAMI (2020)
[8] DeGroot, M.H., Fienberg, S.E.: The comparison and evaluation of forecasters. Journal of the Royal Statistical Society: Series D (The Statistician) (1983)
[9] Delgado-Friedrichs, O., Robins, V., Sheppard, A.: Skeletonization and partitioning of digital images using discrete Morse theory. TPAMI (2014)
[10] Dey, T.K., Wang, J., Wang, Y.: Graph reconstruction by discrete Morse theory. arXiv preprint arXiv:1803.05093 (2018)
[11] Dey, T.K., Wang, J., Wang, Y.: Road network reconstruction from satellite images with machine learning supported by topological methods. In: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (2019)
[12] Eaton-Rosen, Z., Bragman, F., Bisdas, S., Ourselin, S., Cardoso, M.J.: Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions. In: MICCAI (2018)
[13] Edelsbrunner, Letscher, Zomorodian: Topological persistence and simplification. Discrete & Computational Geometry (2002)
[14] Forman, R.: A user's guide to discrete Morse theory. Séminaire Lotharingien de Combinatoire [electronic only] (2002)
[15] Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images-a survey. Computer methods and programs in biomedicine (2012)
[16] Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with bernoulli approximate variational inference. arXiv preprint arXiv:1506.02158 (2015)
[17] Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: ICML (2016)
[18] Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. In: ICML (2017)