Dragan Bošnački

Salome Kazeminia, Max Joosten, Dragan Bosnacki et al.

Automated disease diagnosis using medical image analysis relies on deep learning, often requiring large labeled datasets for supervised model training. Diseases like Acute Myeloid Leukemia (AML) pose challenges due to scarce and costly annotations on a single-cell level. Multiple Instance Learning (MIL) addresses weakly labeled scenarios but necessitates powerful encoders typically trained with labeled data. In this study, we explore Self-Supervised Learning (SSL) as a pre-training approach for MIL-based AML subtype classification from blood smears, removing the need for labeled data during encoder training. We investigate the three state-of-the-art SSL methods SimCLR, SwAV, and DINO, and compare their performance against supervised pre-training. Our findings show that SSL-pretrained encoders achieve comparable performance, showcasing the potential of SSL in MIL. This breakthrough offers a cost-effective and data-efficient solution, propelling the field of AI-based disease diagnosis.

Anton Wijs, Dragan Bošnački, Stefan Edelkamp

These are the proceedings of the Second Workshop on GRAPH Inspection and Traversal Engineering (GRAPHITE 2013), which took place on March 24, 2013 in Rome, Italy, as a satellite event of the 16th European Joint Conferences on Theory and Practice of Software (ETAPS 2013). The topic of the GRAPHITE workshop is graph analysis in all its forms in computer science. Graphs are used to represent data in many application areas, and they are subjected to various computational algorithms in order to acquire the desired information. These graph algorithms tend to have common characteristics, such as duplicate detection to guarantee their termination, independent of their application domain. Over the past few years, it has been shown that the scalability of such algorithms can be dramatically improved by using, e.g., external memory, by exploiting parallel architectures, such as clusters, multi-core CPUs, and graphics processing units, and by using heuristics to guide the search. Novel techniques to further scale graph search algorithms, and new applications of graph search are within the scope of this workshop. Another topic of interest of the event is more related to the structural properties of graphs: which kind of graph characteristics are relevant for a particular application area, and how can these be measured? Finally, any novel way of using graphs for a particular application area is on topic. The goal of this event is to gather scientists from different communities, such as model checking, artificial intelligence planning, game playing, and algorithm engineering, who do research on graph search algorithms, such that awareness of each others' work is increased.