The Human Uterus Cell Atlas (HUTER) project aims to create the single-cell and spatial reference map of the human uterus. HUTER project will provide unprecedented insight at transcriptomic, genomic and spatial changes of this important female organ not only throughout the menstrual cycle but also across lifespan. The human uterus is a flagship reproductive organ with profound implications not only in reproduction but also in women´s health. HUTER can advance the Human Cell Atlas initiative for the exploitation potential in Obstetrics and Gynaecology and biomedicine research areas such as Regenerative Medicine or Reproductive Medicine. The uterus is itself a model for regenerative medicine since (i) endometrial tissue regenerates monthly and its transformation is executed through dynamic changes in states and interactions of multiple cell types, and (ii) myometrial tissue has remarkable regenerative capacity and extensive remodelling throughout pregnancy. Hence, the primary motivation HUTER proposal stems from the need to better understand the human uterus in order to more effectively address uterine diseases that impact women´s health such as myomas or endometriosis and/or might contribute to infertility, infant and maternal mortality and morbidity. HUTER technological and biological platform will be a crucial resource for the scientific and clinical communities to define the cellular basis of health and disease, allowing the rapid development of new diagnosis and prognosis tools and therapeutic advancements in the field.

CHAIMELEON aims to set up a structured repository for health imaging data to be openly reused in AI experimentation for cancer management. An EU-wide repository will be built as a distributed infrastructure in full compliance with legal and ethics regulations in the involved countries. It will build on partner´s experience (e.g. PRIMAGE repository for paediatric cancer and the Euro-BioImaging node for Valencia population, by HULAFE; the Radiomics Imaging Archive by Maastricht University; the national repository DRIM AI France, the Oncology imaging biobank by Pisa University). Clinical partners and external collaborators will populate the Repository with multimodality (MR, CT, PET/CT) imaging and related clinical data for historic and newly diagnosed lung, prostate, colon and rectal cancer patients. A multimodal analytical data engine will facilitate to interpret, extract and exploit the right information stored at the Repository. An ambitious development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and images harmonisation, the latest being of the highest importance for enabling reproducibility of Radiomics when using large multiscanner/multicentre image datasets. The usability and performance of the Repository as a tool fostering AI experimentation will be validated, including a validation subphase by other world-class European AI developers, articulated via the organisation of Open Challenges to the AI Community. A set of selected AI tools will undergo early on-silico validation in observational (non-interventional) clinical studies coordinated by leading experts in Gustave Roussy (lung cancer), San Donato (breast), Sapienza (colon and rectal) and La Fe (prostate) hospitals. Their performance will be assessed, including external independent validation, on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer.