PhD courses and the intersectoral experience:a comprehensive survey

Abstract It has been found that most PhD graduates (>85%) do not achieve a long-term academic career and thus there is a growing need to re-imagine PhD education that incentivizes doctoral students to engage with research consumers, not only within their discipline, but also, across other disciplines and sectors to have real social impact for an improved society. The aim of this work is to identify intersectoral/interdisciplinary courses that are considered to broaden student career outside and inside academia. For this purpose, a survey was designed to identify modules which lead to the improvement of students’ skills while an analysis of their attributes was also performed. Two target groups have been considered: (a) young researchers and (b) program directors each of which can provide different information regarding the courses of interest. 52 students and 11 directors from 5 European Universities, participated in the study. An absence of such courses in the standard PhD program was observed, while any intersectoral/interdisciplinary activities were conducted outside the PhD program, and organized by collaboration of academia and other organizations. The survey findings reveal the need to restructure the PhD programs

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nos. 826494 (PRIMAGE), 952172 (Chaimeleon), 952103 (EuCanImage), 952159 (ProCancer-I), and 952179 (INCISIVE)

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 “AI for Health Imaging” projects, which are all dedicated to the creation of imaging biobanks