@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}
}
@inproceedings {28323,
	title = {Unsupervised Image Segmentation via Self-Supervised Learning Image Classification},
	journal = {MediaEval 2021},
	year = {2022},
	publisher = {CEUR Workshop Proceedings},
	edition = {Working Notes Proceedings of the MediaEval 2021 Workshop},
	abstract = {This paper presents the submission of team Medical-XAI for the Medico: Transparency in Medical Image Segmentation task held at MediaEval 2021. We propose an unsupervised method that utilizes tools from the field of explainable artificial intelligence to create segmentation masks. We extract heat maps, which are useful in order to explain how the {\textquoteleft}black box{\textquoteright} model predicts the category of a certain image, and the segmentation masks are directly derived from the heat maps. Our results show that the created masks can capture the relevant findings to a certain extent using only a small amount of image-level labeled data for the classification model and no segmentation masks at all for the training. This is promising for addressing different challenges within the intersection of artificial intelligence for medicine such as availability of data, cost of labeling and interpretable and explainable results.\&nbsp;},
	keywords = {clustering, Explainable artificial intelligence, Global Features, Grad-CAM, Image segmentation, Medical imaging, Polyp Detection, Self-supervised learning},
	url = {http://ceur-ws.org/Vol-3181/},
	author = {Stor{\r a}s, Andrea}
}
@article {Simula.simula.2672,
	title = {Clustering Deviations for Black Box Regression Testing of Database Applications},
	journal = {Transaction on Reliability},
	year = {2014},
	publisher = {Simula Research Laboratory},
	abstract = {Regression tests often result in many deviations (differences between two system versions), either due to changes or regression faults. Many deviations can be caused by the same change or regression fault. In order for the tester to analyze such deviations efficiently, it would be helpful to accurately group them, such that each group contains deviations representing one unique change or regression fault. We investigate the use of clustering, based on database manipulations and test specifications (from test models), to group regression test deviations according to the faults or changes causing them. We also propose assessment criteria based on the concept of entropy to compare alternative clustering strategies. In the context of database applications, our results show that our clustering approach can indeed serve as an accurate strategy for grouping regression test deviations according to the faults and changes that caused them. Among the four test campaigns assessed, deviations were clustered perfectly for two of them, while for the two others, clusters were all homogenous. Our analysis suggests that this can significantly reduce the effort spent by testers in analyzing regression test deviations, increase their level of confidence under time constraints, and therefore make regression testing more scalable.},
	keywords = {clustering, regression test deviations, Software regression testing, test analysis},
	author = {Rogstad, Erik and Briand, Lionel}
}
