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](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](https://www.nature.com/articles/s41592-024-02580-4) | Segmentation, Tools, Deep Learning |
Dosovitskiy, Alexey. "An image is worth 16x16 words: Transformers for image recognition at scale." *arXiv preprint arXiv:2010.11929* (2020). | [https://arxiv.org/pdf/2010.11929/1000](https://arxiv.org/pdf/2010.11929/1000) | Deep Learning |
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. | [https://arxiv.org/pdf/1505.04597](https://arxiv.org/pdf/1505.04597) | U-Net, Deep Learning, Biomedical image segmentation |
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*. | [https://arxiv.org/pdf/1804.03999](https://arxiv.org/pdf/1804.03999) | Attention U-Net |
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. | [https://arxiv.org/pdf/2306.04905](https://arxiv.org/pdf/2306.04905) | Graph neural networks, U-Net |
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. | [https://mlgdansk.pl/wp-content/uploads/2019/06/MLGdansk63\_27.05.19\_End-to-end\_lung\_cancer\_screening\_with\_three-dimens.pdf](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 |
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. | [https://www.nature.com/articles/s41586-019-1799-6](https://www.nature.com/articles/s41586-019-1799-6) | AI system for breast cancer screening |
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. | [https://proceedings.neurips.cc/paper\_files/paper/2020/file/fcbc95ccdd551da181207c0c1400c655-Paper.pdf](https://proceedings.neurips.cc/paper_files/paper/2020/file/fcbc95ccdd551da181207c0c1400c655-Paper.pdf) | Big self-supervised models |
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). | [https://openaccess.thecvf.com/content/ICCV2021/papers/Azizi\_Big\_Self-Supervised\_Models\_Advance\_Medical\_Image\_Classification\_ICCV\_2021\_paper.pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Azizi_Big_Self-Supervised_Models_Advance_Medical_Image_Classification_ICCV_2021_paper.pdf) | Big self-supervised models |
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. | [https://pmc.ncbi.nlm.nih.gov/articles/PMC10141651/pdf/fonc-13-1109786.pdf](https://pmc.ncbi.nlm.nih.gov/articles/PMC10141651/pdf/fonc-13-1109786.pdf) | Self-supervised medical image segmentation |
| **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](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](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 | [https://pmc.ncbi.nlm.nih.gov/articles/PMC12039951/](https://pmc.ncbi.nlm.nih.gov/articles/PMC12039951/) | multi-scale microscopy, zebrafish |
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. | [https://www.nature.com/articles/nprot.2016.105](https://www.nature.com/articles/nprot.2016.105) | segmentation, data analysis, image based biology |
(2024) Selective uptake into inflamed human intestinal tissue and immune cell targeting by wormlike polymer micelles. *Small* 20(21), 2470162. | [https://www.nature.com/articles/s41592-022-01744-4](https://onlinelibrary.wiley.com/doi/10.1002/smll.202306482) | inflammatory bowel disease (IBD), nanoparticles, shape control |