Nowadays, data collection and analysis are becoming more and more important in a variety of application domains, as long as the novel technologies advance. In the healthcare domain, biomedical image processing is one of the research fields that has benefitted the most by the new availability of big sources of data and recent successes in automated data processing, becoming one of the most known and active producers of digital information.
Different studies show how the quantity of produced electronic data is increasing over time, with some recent estimates indicating it to exceed the Yottabyte size within the next years.
If the growing amount of available data will help the design of effective diseases prevention and therapies assessment procedures, it may result in an increased effort that physicians have to spend to perform a diagnosis. The task is made even more difficult by high inter/intra patients variability, the availability of different image acquisition techniques and the need of being able to take into account media coming from different sensors and sources.
To face this problem, nowadays, radiologists make use of tools that assist in the analysis of biomedical images: these instruments are known as Computer-Aided Detection and Diagnosis (CAD) and, supported by an appropriate and proved medical validity, are widely used in the analysis of complex medical investigations both for the extension of data to be taken into account (MRI\TC\PET) and for an intrinsic uncertainty of the data due to the scanning process (such as UltraSound scans — US).
CAD systems analyze data using strict mathematical patterns, according to well-defined and deterministic algorithms. This characteristic allows to remove the difficulties due to inter- and intraobserver variability, represented by different evaluations of the same region, under the same assumptions, by the same doctor on different moments, and different evaluations of the same region by different doctors.
The aim of this workshop is to gather recent advances in the biomedical images processing field to help advance the scientific research within the broad field of medical imaging using machine learning, deep learning and big data techniques. In particular, the aim is to analyze how these techniques can be applied to the entire medical images processing chain, from acquisition to image retrieval, from segmentation to disease prediction.
Topics of interest include, but are not limited to:
● Biomedical Image Processing
○ Machine and Deep Learning techniques for image analysis (ie, organ and lesion segmentation, tumors classification, cell segmentation, etc)
○ Motion Correction and Image Co-Registration Techniques
○ 3D reconstruction and pre-surgery planning
○ Computer Aided Detection and Diagnosis Systems
● Big Data Analytics in eHealth systems
○ Multimodality fusion (e.g., MRI/PET, PET/CT, X-ray/ultrasound) for diagnosis, image analysis, and image-guided intervention
○ Image retrieval (e.g., context-based retrieval, lesion similarity)
○ Big Data architecture for image analysis to predictive analysis aim
○ Multimedia data processing to improve image analysis effectiveness
● Telemedicine
○ Advanced architecture for biomedical image remote processing, elaboration and transmission
○ 3D Vision, Virtual, Augmented and Mixed Reality application for remote surgery
○ Image processing techniques for privacy-preserving AI in medicine
Paper submission deadline: June 9th June 23th, 2019
Paper notification: July 6th, 2019
Camera-ready paper due: July 17th, 2019
Registration due: July 19th (early) - https://event.unitn.it/iciap2019/#registration (NOTE: look for workshop registration)
Submitted papers must be unpublished and not considered elsewhere for publication. Submissions will undergo a rigorous review process handled by the Technical Program Committee. Papers will be selected based on their originality, significance, relevance, and clarity of presentation. Only electronic submissions in PDF format through the EasyChair submission site (https://easychair.org/conferences/?conf=ebadle2019) will be considered. Papers must be in English, up to 8 pages in Springer format, including references and appendices. The Springer LaTeX and Microsoft Word templates, as well as formatting guidelines, can be found on the paper submission instructions available at the main conference website.