@inproceedings {42538,
	title = {Automatic Unsupervised Clustering of Videos of the Intracytoplasmic Sperm Injection (ICSI) Procedure},
	journal = {NAIS: Symposium of the Norwegian AI Society },
	volume = {1650},
	year = {2022},
	pages = {1-11},
	publisher = {NAIS 2022},
	abstract = {The\&nbsp;in vitro\&nbsp;fertilization procedure called intracytoplasmic sperm injection can be used to help fertilize an egg by injecting a single sperm cell directly into the cytoplasm of the egg. In order to evaluate, refine and improve the method in the fertility clinic, the procedure is usually observed at the clinic. Alternatively, a video of the procedure can be examined and labeled in a time-consuming process. To reduce the time required for the assessment, we propose an unsupervised method that automatically clusters video frames of the intracytoplasmic sperm injection procedure. Deep features are extracted from the video frames and form the basis for a clustering method. The method provides meaningful clusters representing different stages of the intracytoplasmic sperm injection procedure. The clusters can lead to more efficient examinations and possible new insights that can improve clinical practice. Further on, it may also contribute to improved clinical outcomes due to increased understanding about the technical aspects and better results of the procedure. Despite promising results, the proposed method can be further improved by increasing the amount of data and exploring other types of features.},
	keywords = {clustering, Computer Vision and Pattern Recognition (cs.CV), human reproduction, medical videos, Unsupervised learning},
	isbn = {978-3-031-17029-4},
	doi = {10.1007/978-3-031-17030-0_9},
	author = {Stor{\r a}s, Andrea and Riegler, Michael and Trine B. {Haugen} and Thambawita, Vajira and Hicks, Steven and Hugo Lewi {Hammer} and Kakulavarapu, Radhika and Halvorsen, P{\r a}l and Stensen, Mette}
}
@misc {https://doi.org/10.48550/arxiv.2104.05642,
	title = {Common Limitations of Image Processing Metrics: A Picture Story},
	year = {2022},
	publisher = {arXiv},
	abstract = {While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.},
	keywords = {Computer Vision and Pattern Recognition (cs.CV), electronic engineering, FOS: Computer and information sciences, FOS: Electrical engineering, Image and Video Processing (eess.IV), information engineering},
	doi = {10.48550/ARXIV.2104.05642},
	url = {https://arxiv.org/abs/2104.05642},
	author = {Reinke, Annika and Minu D. {Tizabi} and Carole H. {Sudre} and Eisenmann, Matthias and R{\"a}dsch, Tim and Baumgartner, Michael and Acion, Laura and Antonelli, Michela and Arbel, Tal and Bakas, Spyridon and Bankhead, Peter and Benis, Arriel and Cardoso, Jorge and Cheplygina, Veronika and Cimini, Beth and Gary S. {Collins} and Farahani, Keyvan and Glocker, Ben and Godau, Patrick and Hamprecht, Fred and Daniel A. {Hashimoto} and Heckmann-N{\"o}tzel, Doreen and Michael M. {Hoffmann} and Huisman, Merel and Isensee, Fabian and Jannin, Pierre and Charles E. {Kahn} and Karargyris, Alexandros and Karthikesalingam, Alan and Kainz, Bernhard and Kavur, Emre and Kenngott, Hannes and Kleesiek, Jens and Kooi, Thijs and Kozubek, Michal and Kreshuk, Anna and Kurc, Tahsin and Bennett A. {Landman} and Litjens, Geert and Madani, Amin and Maier-Hein, Klaus and Anne L. {Martel} and Mattson, Peter and Meijering, Erik and Menze, Bjoern and Moher, David and Karel G. M. {Moons} and M{\"u}ller, Henning and Nickel, Felix and Petersen, Jens and Polat, Gorkem and Rajpoot, Nasir and Reyes, Mauricio and Rieke, Nicola and Riegler, Michael and Rivaz, Hassan and Saez-Rodriguez, Julio and Clarisa Sanchez {Gutierrez} and Schroeter, Julien and Saha, Anindo and Shetty, Shravya and Stieltjes, Bram and Ronald M. {Summers} and Abdel A. {Taha} and Sotirios A. {Tsaftaris} and van Ginneken, Bram and Varoquaux, Ga{\"e}l and Wiesenfarth, Manuel and Ziv R. {Yaniv} and Kopp-Schneider, Annette and J{\"a}ger, Paul and Maier-Hein, Lena}
}
