MFreidank/glenda

Tasks

• image-classification

Citation text from the card

@inproceedings{10.1007/978-3-030-37734-2_36,
	abstract = {Gynecologic laparoscopy as a type of minimally invasive surgery (MIS) is performed via a live feed of a patient's abdomen surveying the insertion and handling of various instruments for conducting treatment. Adopting this kind of surgical intervention not only facilitates a great variety of treatments, the possibility of recording said video streams is as well essential for numerous post-surgical activities, such as treatment planning, case documentation and education. Nonetheless, the process of manually analyzing surgical recordings, as it is carried out in current practice, usually proves tediously time-consuming. In order to improve upon this situation, more sophisticated computer vision as well as machine learning approaches are actively developed. Since most of such approaches heavily rely on sample data, which especially in the medical field is only sparsely available, with this work we publish the Gynecologic Laparoscopy ENdometriosis DAtaset (GLENDA) -- an image dataset containing region-based annotations of a common medical condition named endometriosis, i.e. the dislocation of uterine-like tissue. The dataset is the first of its kind and it has been created in collaboration with leading medical experts in the field.},
	address = {Cham},
	author = {Leibetseder, Andreas and Kletz, Sabrina and Schoeffmann, Klaus and Keckstein, Simon and Keckstein, J{\"o}rg},
	booktitle = {MultiMedia Modeling},
	editor = {Ro, Yo

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