@article{graham2019hover,
  title={Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images},
  author={Graham, Simon and Vu, Quoc Dang and Raza, Shan E Ahmed and Azam, Ayesha and Tsang, Yee Wah and Kwak, Jin Tae and Rajpoot, Nasir},
  journal={Medical image analysis},
  volume={58, 101563},
  year={2019},
  publisher={Elsevier}
}

@article{cellvit,
  title = {CellViT: Vision Transformers for precise cell segmentation and classification},
  journal = {Medical Image Analysis},
  volume = {94},
  pages = {103143},
  year = {2024},
  issn = {1361-8415},
  doi = {https://doi.org/10.1016/j.media.2024.103143},
  url = {https://www.sciencedirect.com/science/article/pii/S1361841524000689},
  author = {F. Hörst and M. Rempe and L. Heine and C. Seibold and J. Keyl and G. Baldini and S. Ugurel and Je. Siveke and B. Grünwald and Jan E. and J. Kleesiek}
}

@article{chen2025histonext,
  author    = {Chen, J. and Wang, R. and Dong, W. and others},
  title     = {HistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification},
  journal   = {BMC Medical Imaging},
  volume    = {25},
  number    = {9},
  year      = {2025},
  doi       = {10.1186/s12880-025-01550-2},
  url       = {https://doi.org/10.1186/s12880-025-01550-2}
}

@inproceedings{anglada2025dualunet,
    title={Two Heads Are Enough: DualU-Net, a Fast and Efficient Architecture for Nuclei Instance Segmentation},
    author={David Anglada-Rotger and Berta Jansat and Ferran Marques and Montse Pard{\`a}s},
    booktitle={Medical Imaging with Deep Learning},
    year={2025},
    url={https://openreview.net/forum?id=lK0CklgxQd}
}

@inproceedings{gal2016dropout,
  author    = {Gal, Yarin and Ghahramani, Zoubin},
  title     = {Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning},
  booktitle = {Proc. 33rd International Conf. on Machine Learning},
  year      = {2016},
  pages     = {1050--1059}
}

@inproceedings{lakshminarayanan2017deep,
  author    = {Lakshminarayanan, B. and Pritzel, A. and Blundell, C.},
  title     = {Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles},
  booktitle = {Advances in Neural Information Processing Systems},
  year      = {2017},
  volume    = {30}
}

@article{ovadia2020can,
  author  = {Ovadia, Yaniv and Fertig, Emily and Ren, Daisy et al.},
  title   = {Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty under Dataset Shift},
  journal = {Advances in Neural Information Processing Systems},
  year    = {2020},
  volume  = {33}
}

@article{kohl2018probunet,
  author       = {Simon A. A. Kohl and Bernardino Romera{-}Paredes and Clemens Meyer and Jeffrey De Fauw and Joseph R. Ledsam and Klaus H. Maier{-}Hein and S. M. Ali Eslami and Danilo Jimenez Rezende and Olaf Ronneberger},
  title        = {A Probabilistic U-Net for Segmentation of Ambiguous Images},
  journal      = {CoRR},
  volume       = {abs/1806.05034},
  year         = {2018},
  url          = {http://arxiv.org/abs/1806.05034}
}

@misc{baumgartner2019phiseg,
  title={PHiSeg: Capturing Uncertainty in Medical Image Segmentation}, 
  author={Christian F. Baumgartner and Kerem C. Tezcan and Krishna Chaitanya and Andreas M. Hötker and Urs J. Muehlematter and Khoschy Schawkat and Anton S. Becker and Olivio Donati and Ender Konukoglu},
  year={2019},
  eprint={1906.04045},
  archivePrefix={arXiv},
  primaryClass={eess.IV},
  url={https://arxiv.org/abs/1906.04045}
}

@InProceedings{tan2025uncert,
  author="Tan, Hai Siong and Wang, Kwancheng and McBeth, Rafe",
  title="Uncertainty-Error Correlations in Evidential Deep Learning Models for Biomedical Segmentation",
  booktitle="Technologies and Applications of Artificial Intelligence",
  year="2025",
  pages="91--105",
  publisher="Springer"
}

@inproceedings{Anglada-Rotger_2024_CVPR,
  author={Anglada-Rotger, David and Sala, Julia and Marques, Ferran and Salembier, Philippe and Pardàs, Montse},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, 
  title={Enhancing Ki-67 Cell Segmentation with Dual U-Net Models: A Step Towards Uncertainty-Informed Active Learning}, 
  year={2024},
  pages={5026--5035}
}

@inproceedings{guo2017calibration,
  author    = {Chuan Guo and Geoff Pleiss and Yu Sun and Kilian Q. Weinberger},
  title     = {On Calibration of Modern Neural Networks},
  booktitle = {Proceedings of the 34th International Conference on Machine Learning (ICML)},
  pages     = {1321--1330},
  year      = {2017}
}

@inproceedings{kumar2018trainable,
  author    = {Kumar, A. and Sarawagi, S. and Jain, U.},
  title     = {Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings},
  booktitle = {Proceedings of the International Conference on Machine Learning},
  year      = {2018},
  pages     = {2805--2814}
}

@article{mukhoti2019focal,
  author       = {Jishnu Mukhoti and Viveka Kulharia and Amartya Sanyal and Stuart Golodetz and Philip H. S. Torr and Puneet K. Dokania},
  title        = {Calibrating Deep Neural Networks using Focal Loss},
  journal      = {CoRR},
  volume       = {abs/2002.09437},
  year         = {2020},
  url          = {https://arxiv.org/abs/2002.09437}
}

@article{lin2025bscegra,
  author  = {Lin, Jinxu and Tao, Linwei and Dong, Minjing and Xu, Chang},
  title   = {Uncertainty--Weighted Gradients for Model Calibration},
  journal = {CVPR},
  year    = {2025}
}

@inproceedings{gal2017uncertainties,
 author = {Kendall, Alex and Gal, Yarin},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?},
 url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/2650d6089a6d640c5e85b2b88265dc2b-Paper.pdf},
 volume = {30},
 year = {2017}
}

download as .bib file

@article{sensoy2018evidential,
  author       = {Murat Sensoy and
                  Melih Kandemir and
                  Lance M. Kaplan},
  title        = {Evidential Deep Learning to Quantify Classification Uncertainty},
  journal      = {CoRR},
  volume       = {abs/1806.01768},
  year         = {2018},
  url          = {http://arxiv.org/abs/1806.01768},
  eprinttype    = {arXiv},
  eprint       = {1806.01768},
  timestamp    = {Sat, 30 Sep 2023 10:08:09 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1806-01768.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{
sciadv_gomariz2022,
author = {Alvaro Gomariz  and Tiziano Portenier  and César Nombela-Arrieta  and Orcun Goksel },
title = {Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression},
journal = {Science Advances},
volume = {8},
number = {5},
pages = {eabi8295},
year = {2022},
doi = {10.1126/sciadv.abi8295},
URL = {https://www.science.org/doi/abs/10.1126/sciadv.abi8295},
eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.abi8295}}

@InProceedings{tan2024edlbiomed,
author="Tan, Hai Siong
and Wang, Kwancheng
and McBeth, Rafe",
editor="Chu, Wei-Ta
and Shen, Chih-Ya
and Shuai, Hong-Han",
title="Uncertainty-Error Correlations in Evidential Deep Learning Models for Biomedical Segmentation",
booktitle="Technologies and Applications of Artificial Intelligence",
year="2025",
publisher="Springer Nature Singapore",
address="Singapore",
pages="91--105",
isbn="978-981-96-4589-3"
}

@INPROCEEDINGS{ancha2024icra,
  author={Ancha, Siddharth and Osteen, Philip R. and Roy, Nicholas},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={Deep Evidential Uncertainty Estimation for Semantic Segmentation under Out-Of-Distribution Obstacles}, 
  year={2024},
  volume={},
  number={},
  pages={6943-6951},
  keywords={Training;Analytical models;Uncertainty;Accuracy;Navigation;Semantic segmentation;Estimation},
  doi={10.1109/ICRA57147.2024.10611342}}

@article{jiang2024aaai, title={Evidential Uncertainty-Guided Mitochondria Segmentation for 3D EM Images}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/28287}, DOI={10.1609/aaai.v38i5.28287}, number={5}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Shi, Ruohua and Duan, Lingyu and Huang, Tiejun and Jiang, Tingting}, year={2024}, month={Mar.}, pages={4847-4855} }


@inbook{li2025miccai,
   title={Uncertainty-Supervised Interpretable and Robust Evidential Segmentation},
   ISBN={9783032051851},
   ISSN={1611-3349},
   url={http://dx.doi.org/10.1007/978-3-032-05185-1_62},
   DOI={10.1007/978-3-032-05185-1_62},
   booktitle={Medical Image Computing and Computer Assisted Intervention – MICCAI 2025},
   publisher={Springer Nature Switzerland},
   author={Li, Yuzhu and Sui, An and Wu, Fuping and Zhuang, Xiahai},
   year={2025},
   month=sep, pages={649–658} }

@misc{he2024bibm,
      title={Mutual Evidential Deep Learning for Medical Image Segmentation}, 
      author={Yuanpeng He and Yali Bi and Lijian Li and Chi-Man Pun and Wenpin Jiao and Zhi Jin},
      year={2025},
      eprint={2505.12418},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2505.12418}, 
}


@article{gamper2020pannuke,
  author  = {Jevgenij Gamper and
             Navid Alemi Koohbanani and
             Ksenija Benes and
             Simon Graham and
             Mostafa Jahanifar and
             Syed Ali Khurram and
             Ayesha Azam and
             Katherine Hewitt and
             Nasir Rajpoot},
  title   = {PanNuke Dataset Extension, Insights and Baselines},
  journal = {arXiv preprint arXiv:2003.10778},
  year    = {2020}
}

@InProceedings{uncertainyerror2025Tan,
author="Tan, Hai Siong
and Wang, Kwancheng
and McBeth, Rafe",
title="Uncertainty-Error Correlations in Evidential Deep Learning Models for Biomedical Segmentation",
booktitle="Technologies and Applications of Artificial Intelligence",
year="2025",
publisher="Springer Nature Singapore",
address="Singapore",
pages="91--105",
isbn="978-981-96-4589-3"
}

@InProceedings{nixon19,
author = {Nixon, Jeremy and Dusenberry, Michael W. and Zhang, Linchuan and Jerfel, Ghassen and Tran, Dustin},
title = {Measuring Calibration in Deep Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

@article{corr1019Laves,
  author       = {Max{-}Heinrich Laves and
                  Sontje Ihler and
                  Karl{-}Philipp Kortmann and
                  Tobias Ortmaier},
  title        = {Well-calibrated Model Uncertainty with Temperature Scaling for Dropout
                  Variational Inference},
  journal      = {CoRR},
  volume       = {abs/1909.13550},
  year         = {2019},
  url          = {http://arxiv.org/abs/1909.13550},
  eprinttype    = {arXiv},
  eprint       = {1909.13550},
  timestamp    = {Sun, 02 Oct 2022 15:31:57 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1909-13550.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{edl_reg,
  author       = {Alexander Amini and
                  Wilko Schwarting and
                  Ava Soleimany and
                  Daniela Rus},
  title        = {Deep Evidential Regression},
  journal      = {CoRR},
  volume       = {abs/1910.02600},
  year         = {2019},
  url          = {http://arxiv.org/abs/1910.02600},
  eprinttype    = {arXiv},
  eprint       = {1910.02600},
  timestamp    = {Wed, 09 Oct 2019 14:07:58 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1910-02600.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{digipatics,
  author    = {Temprana-Salvador, J and L{\'o}pez-Garc{\'i}a, P and Castellv{\'i}, J and others},
  title     = {DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals-Planification, Implementation, and Preliminary Results},
  journal   = {Diagnostics (Basel)},
  volume    = {12},
  number    = {4},
  pages     = {852},
  year      = {2022},
  doi       = {10.3390/diagnostics12040852}
}

@article{xie2018microscopy,
  title={Microscopy cell counting and detection with fully convolutional regression networks},
  author={Xie, Weidi and Noble, J Alison and Zisserman, Andrew},
  journal={Computer methods in biomechanics and biomedical engineering: Imaging \& Visualization},
  volume={6},
  number={3},
  pages={283--292},
  year={2018},
  publisher={Taylor \& Francis}
}

@inproceedings{
chen2024redl,
title={R-{EDL}: Relaxing Nonessential Settings of Evidential Deep Learning},
author={Mengyuan Chen and Junyu Gao and Changsheng Xu},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=Si3YFA641c}
}

@ARTICLE{chen2025reedl,
  author={Chen, Mengyuan and Gao, Junyu and Xu, Changsheng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Revisiting Essential and Nonessential Settings of Evidential Deep Learning}, 
  year={2025},
  volume={47},
  number={10},
  pages={8658-8673},
  doi={10.1109/TPAMI.2025.3583410}}

@inproceedings{shen2024uncertainty,
  title={Are uncertainty quantification capabilities of evidential deep learning a mirage?},
  author={Shen, Maohao and Ryu, Jongha Jon and Ghosh, Soumya and Bu, Yuheng and Sattigeri, Prasanna and Das, Subhro and Wornell, Gregory},
  booktitle={Advances in Neural Information Processing Systems},
  volume={37},
  pages={107830--107864},
  year={2024}
}

@article{xie2017aggregated,
  author       = {Saining Xie and
                  Ross B. Girshick and
                  Piotr Doll{\'{a}}r and
                  Zhuowen Tu and
                  Kaiming He},
  title        = {Aggregated Residual Transformations for Deep Neural Networks},
  journal      = {CoRR},
  volume       = {abs/1611.05431},
  year         = {2016},
  url          = {http://arxiv.org/abs/1611.05431}
}