@misc{16407,
  keywords = {clustering, Polyp Detection, Global Features, Medical imaging, Image segmentation, Self-supervised learning, Grad-CAM, Explainable artificial intelligence},
  author = {Andrea Stor{\r a}s},
  title = {Unsupervised Image Segmentation via Self-Supervised Learning Image Classification},
  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.},
  year = {2022},
  journal = {MediaEval 2021},
  edition = {Working Notes Proceedings of the MediaEval 2021 Workshop},
  publisher = {CEUR Workshop Proceedings},
  url = {http://ceur-ws.org/Vol-3181/},
}