AuthorsA. Storås
TitleUnsupervised Image Segmentation via Self-Supervised Learning Image Classification
AfilliationMachine Learning
Project(s)Department of Holistic Systems
StatusAccepted
Publication TypeProceedings, refereed
Year of Publication2022
Conference NameMediaEval 2021
EditionWorking Notes Proceedings of the MediaEval 2021 Workshop
PublisherCEUR Workshop Proceedings
Keywordsclustering, Explainable artificial intelligence, Global Features, Grad-CAM, Image segmentation, Medical imaging, Polyp Detection, Self-supervised learning
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 `black box' 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. 

URLhttps://2021.multimediaeval.com/paper12.pdf
Citation Key28323

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