Paper suggestions: image analysis
Here you can find a list of suggested papers that involve image analysis. You can also find suitable papers on your own (ask Thilo Figge).
If you are unhappy with a paper you can always ask Thilo Figge to look for a replacement topic.
You will have to write a one-page summary of the paper and send it to Thilo Figge a few days before your presentation.
Deep Learning in Microscopy
| Citation | Link | Keywords |
| Stringer, C., Wang, T., Michaelos, M. et al. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021). | https://www.nature.com/articles/s41592-020-01018-x | Segmentation, Tools, Deep Learning |
| Archit, A., Freckmann, L., Nair, S. et al. Segment Anything for Microscopy. Nat Methods 22, 579–591 (2025). | https://www.nature.com/articles/s41592-024-02580-4 | Segmentation, Tools, Deep Learning |
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Dosovitskiy, Alexey. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv preprint arXiv:2010.11929 (2020).
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https://arxiv.org/pdf/2010.11929/1000 |
Deep Learning |
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Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Cham: Springer international publishing.
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https://arxiv.org/pdf/1505.04597 |
U-Net, Deep Learning, Biomedical image segmentation |
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Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B. and Glocker, B., 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999.
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https://arxiv.org/pdf/1804.03999 |
Attention U-Net |
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Jiang, J., Chen, X., Tian, G. and Liu, Y., 2023, April. ViG-UNet: vision graph neural networks for medical image segmentation. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE.
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https://arxiv.org/pdf/2306.04905 |
Graph neural networks, U-Net |
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Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher, J.J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G. and Naidich, D.P., 2019. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature medicine, 25(6), pp.954-961.
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https://mlgdansk.pl/wp-content/uploads/2019/06/MLGdansk63_27.05.19_End-to-end_lung_cancer_screening_with_three-dimens.pdf |
Deep Learning (3D), lung cancer detection, computed tomography |
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McKinney, S.M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G.S., Darzi, A. and Etemadi, M., 2020. International evaluation of an AI system for breast cancer screening. Nature, 577(7788), pp.89-94.
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https://www.nature.com/articles/s41586-019-1799-6 |
AI system for breast cancer screening |
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Chen, T., Kornblith, S., Swersky, K., Norouzi, M. and Hinton, G.E., 2020. Big self-supervised models are strong semi-supervised learners. Advances in neural information processing systems, 33, pp.22243-22255.
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https://proceedings.neurips.cc/paper_files/paper/2020/file/fcbc95ccdd551da181207c0c1400c655-Paper.pdf |
Big self-supervised models |
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Azizi, S., Mustafa, B., Ryan, F., Beaver, Z., Freyberg, J., Deaton, J., Loh, A., Karthikesalingam, A., Kornblith, S., Chen, T. and Natarajan, V., 2021. Big self-supervised models advance medical image classification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3478-3488).
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https://openaccess.thecvf.com/content/ICCV2021/papers/Azizi_Big_Self-Supervised_Models_Advance_Medical_Image_Classification_ICCV_2021_paper.pdf |
Big self-supervised models |
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Zhao, L., Jia, C., Ma, J., Shao, Y., Liu, Z. and Yuan, H., 2023. Medical image segmentation based on self-supervised hybrid fusion network. Frontiers in Oncology, 13, p.1109786.
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10141651/pdf/fonc-13-1109786.pdf |
Self-supervised medical image segmentation |
Imaging and image analysis in Biological Systems
| Citation | Link | Keywords |
| van Ooijen, Hanna et al.
A thermoplastic chip for 2D and 3D correlative assays combining screening and high-resolution imaging of immune cell responses. Cell Reports Methods, Volume 5, Issue 1, 100965 |
https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(25)00001-3 |
imaging, microwell, correlative imaging, high-resolution, tumor microenvironment, natural killer cell |
| et al. (2025). A fluorescence lifetime separation approach for FLIM live-cell imaging. Journal of Microscopy, 1–16. | https://onlinelibrary.wiley.com/doi/full/10.1111/jmi.70036 | FLIM, live cell imaging |
| Imaging of cellular dynamics from a whole organism to subcellular scale with self-driving, multiscale microscopy
S Daetwyler, H Mazloom-Farsibaf, FY Zhou, D Segal, E Sapoznik, B Chen, ...
Nature methods 22 (3), 569-578
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https://pmc.ncbi.nlm.nih.gov/articles/PMC12039951/ | multi-scale microscopy, zebrafish |
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Bray, Mark-Anthony, et al. "Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes." Nature protocols 11.9 (2016): 1757-1774.
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https://www.nature.com/articles/nprot.2016.105 |
segmentation, data analysis, image based biology |
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(2024) Selective uptake into inflamed human intestinal tissue and immune cell targeting by wormlike polymer micelles. Small 20(21), 2470162.
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https://www.nature.com/articles/s41592-022-01744-4 |
inflammatory bowel disease (IBD), nanoparticles, shape control |