Bibliography

[FBPTGonzalezM+22]

D. Franco-Barranco, J. Pastor-Tronch, A. González-Marfil, A. Muñoz-Barrutia, and I. Arganda-Carreras. Deep learning based domain adaptation for mitochondria segmentation on em volumes. Computer Methods and Programs in Biomedicine, 222:106949, 2022.

[FBMunozBAC21]

Daniel Franco-Barranco, Arrate Muñoz-Barrutia, and Ignacio Arganda-Carreras. Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes. Neuroinformatics, Dec 2021. URL: https://doi.org/10.1007/s12021-021-09556-1, doi:10.1007/s12021-021-09556-1.

[HCX+22]

Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 16000–16009. 2022.

[KBJ19]

Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. Noise2void-learning denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2129–2137. 2019.

[LWP+21]

Zudi Lin, Donglai Wei, Mariela D. Petkova, Yuelong Wu, Zergham Ahmed, Krishna Swaroop K, Silin Zou, Nils Wendt, Jonathan Boulanger-Weill, Xueying Wang, Nagaraju Dhanyasi, Ignacio Arganda-Carreras, Florian Engert, Jeff Lichtman, and Hanspeter Pfister. Nucmm dataset: 3d neuronal nuclei instance segmentation at sub-cubic millimeter scale. 2021. arXiv:2107.05840.

[WSH+20]

Martin Weigert, Uwe Schmidt, Robert Haase, Ko Sugawara, and Gene Myers. Star-convex polyhedra for 3d object detection and segmentation in microscopy. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 3666–3673. 2020.

[YSW+21]

Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, and Bingbing Ni. Medmnist v2: a large-scale lightweight benchmark for 2d and 3d biomedical image classification. arXiv preprint arXiv:2110.14795, 2021.

[ZWKrahenbuhl19]

X. Zhou, D. Wang, and P. Krähenbühl. Objects as points. arXiv preprint arXiv:1904.07850, 2019.