Introduction
The ExaMode1 project is a research project funded by the European Union’s Horizon 2020 framework program (running from 2019–2023). Seven complementary European research partners work on reaching the main project objectives, notably two Universities, two medical centers, two companies and a national supercomputing center.
1 http://www.examode.eu/
The goals of the project are linked to very-large-scale data analysis, for which histopathology is an almost perfect use case with extremely large images and very large numbers being produced in many hospitals. The very-large-scale analysis aims to build data-driven decision support tools for clinicians. A challenge in image-based decision support is the manual annotation of images to train deep learning models, which is extremely expensive and has some subjectivity. For this reason, the project aims to use weak annotations [2, 3], so slide level labels for staging/grading/etc. instead of strong annotations, so annotated pixels. All data of a case, such as text reports and images are combined for multimodal data analysis. Text is mapped onto semantics for a better understanding, and domain specific ontologies were developed for the project use cases [4]. Multimodal learning aims to gain from the text and semantics and learn decision support on the images [1]. This has the advantage that learning does not require any human intervention if sufficiently large data sets are available.
Project structure
The entire physical computing infrastructure (storage and CPU/GPU computing) of the project is at SURFSARA, the Dutch national supercomputing center, which allows to develop scalable approaches for thousands of histopathology images (of around 10 GB each). Safe storage and secure computing with strict access control are also guaranteed with this. Figure 1 details the partners of the project.
