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. 

Most of today's IP traffic is cloud traffic. Due to a vast, complex and non-transparent Internet infrastructure, securely accessing and delegating data is not a trivial task. Existing technologies of Information-Centric Networking (ICN) make content distribution and access easy while primarily relying on the existing cloud-based security features. The primary aim of ICN is to make data independent of its storage location and application. ICN builds upon traditional distributed computing, which means ICN platforms also can suffer from similar data security issues as distributed computing platforms. We present our ongoing work to develop a secure, proactive data distribution framework. The framework answers the research question, i.e., How to extend online data protection with a secure data distribution model for the ICN platform? Our framework adds a data protection layer over the content distribution network, using blockchain and relying on the fog to distribute the contents with low latency. Our framework is different from the existing works in multiple aspects, such as i) data are primarily distributed from the fog nodes, ii) blockchain is used to protect data and iii) blockchain allows statistical and other information sharing among stakeholders (such as content creators) following access rights. Sharing statistics about content distribution activity can bring transparency and trustworthiness among the stakeholders, including the subscribers, into the ICN platforms. We showed such a framework is possible by presenting initial performance results and our reflections while implementing it on a cloud/fog research testbed.