This workshop aims at collecting articles reporting activities related or employing the DeepHealth technologies. These may be:
Algorithmic improvements to machine learning or computer vision, contributed to the DeepHealth libraries, in CPU, GPU and/or FPGA.
Novel approaches or features/descriptors realized and included into the DeepHealth libraries.
Applications of the DeepHealth toolkit, to real use cases in the biomedical domain.
Reports on medical adoption of the DeepHealth framework, also with the use of the frontend/backend system.
Success stories and datasets contributions with guides to reproduce the experiments, along with the corresponding open source repositories.
Healthcare is one of the key sectors of the global economy, especially in Europe. Any improvement in healthcare systems has a high impact on the welfare of the society. The use of technologies in health is clearly a strong path to more efficient healthcare, benefitting both individual people and the publicbudgets. European public health systems are generating large datasets of biomedical data in general, and images in particular, as most medical examinations use image-based processes. These datasets are in continuous growth and constitute a large unexploited knowledge database since most of its value comes from the interpretations of the experts. Nowadays, this process is generally performed manually and global knowledge sharing is complex.
In the context of automating and accelerating the analysis of the health data and processes, health scientific discovery and innovation are expected to quickly move forward under the so-called “fourth paradigm of science”, which relies on unifying the traditionally separated and heterogeneous high-performance computing and big data analytics environments. Under this paradigm, the DeepHealth project was started to provide HPC computing power at the service of biomedical applications, and to apply Deep Learning (DL) techniques on large and complex biomedical datasets supporting new and more efficient ways for diagnosing, monitoring and treating diseases.
The DeepHealth project provided a unified framework aimed at exploiting heterogeneous HPC andBig Data architectures, assembled with state-of-the-art techniques in Deep Learning and Computer Vision. In particular, two new libraries, the European Distributed Deep Learning Library (EDDL) and the European Computer Vision Library (ECVL) have been developed together with the DeepHealth framework (https://github.com/deephealthproject) for manipulating and processing the images in a more efficient way and thus, for increasing the productivity of professionals working on biomedical images.
Accepted papers will be included in the ICIAP 2021 proceedings, which will be published by Springer as Lecture Notes in Computer Science series (LNCS).
When preparing your contribution, please follow the guidelines provided by Springer. The minimum number of pages is 8. The maximum number of pages is 10 + 2 pages for references. Each contribution will be reviewed based on originality, significance, clarity, soundness, relevance and technical content.
Submissions must be uploaded through EasyChair at https://easychair.org/conferences/?conf=dhw2022
Once accepted, the presence of at least one author at the event and the oral presentation of the paper are expected.