dataesr/scientific-paragraphs-categorization huggingface dataset:
Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e. g. , The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist.
Background Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of disease categorization and populationbased treatments to the era of personalized medicine, healthcare, and individual prevention. In particular, in the domain of cancer detection, treatment, and management, AI can solve several pressing and unmet clinical needs improving not only patient survival but also their quality of life. Worldwide interest in AI applications, imaging being one of the most prominent fields of application, is high and growing rapidly, fueled by the availability of large datasets (big data), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods, there are many opportunities and challenges for the imaging community, including the development of a common nomenclator, better ways to store, curate, and share the now limited imaging datasets, and also standards for validating AI programs used across different imaging platforms and patient populations  [1].
The overarching vision of the platforms developed by these projects can be further specialized and made more concrete as follows: -Compliant with FAIR data principles: the platforms should be built on FAIR-compliant and secure design and support data governance that enables sustainable cross-border connection of pan-cancer image data sources -Built for all involved stakeholders: the designed systems should be primarily aimed for use by clinicians, researchers, AI modellers, and innovators. It is essential for such platforms to promote technologies and tools necessary for data analysis, in compliance with the relevant legal, ethical, quality and interoperability requirements and standards -Achieving interoperability and linkage of information: the designed infrastructures should accommodate interoperability and linkage between fragmented data silos -Compliant with data quality standards: support for actions aiming to create or extend cancer image data sources (including image annotation) and/or to adapt existing data (legacy data) in accordance with agreed data quality standards and legal requirements -Compliant with GDPR and legal requirements: the platforms must be designed bearing in mind the data protection principles provided for by GDPR, while ensuring the integrity and confidentiality of the data.
There are many challenges in trying to create such an infrastructure, and many obstacles should be overcome. This paper focuses on highlighting the various aspects involved in their design, present challenges, and potential solutions of the five projects positioning them based on the changes made for architecture, data models, GDPR considerations, and curation processes they adopt. The remainder of this paper is structured as follows. First, we provide an overview of existing approaches to building such infrastructures for data storage, curation, and management for AI developments in cancer imaging, focusing on the data models used, on security aspects, and curation tools required from such infrastructures. Second, we present the common challenges of these projects. Finally, we present some conclusive remarks.
CHAIMELEON CHAIMELEON aims to set up an EU-wide infrastructure, methodology, and tools to overcome the lack of availability of large quality controlled collected datasets and the heterogeneity of data and practices across institutions. Ultimately, it will enhance the reproducibility of radiomics features and achieve explainable AI for improved cancer management applications. These tools will be validated in the application context of four organs: lung, colorectal, breast, and prostate. CHAIME-LEON adopts a hybrid architecture composed of both local data warehouses and a central repository with anonymized data. More details on the project architecture are provided in Additional file 1. The central repository will provide storage resources to share health images, related clinical data, and molecular data from pathology and liquid biopsy samples.
CHAIMELEON data models Clinical data in CHAIMELEON are collected through electronic clinical report forms (eCRFs) and stored either in the local or in central database. OMOP was chosen as the Common Data Model for the local data warehouses and the structure of the eCRF. OMOP CDM is a commonly accepted international standard for working in observational studies, which facilitates interoperability between centers. OMOP CDM allows working with extensions, which gives greater adaptability when working with heterogeneous data. Within CHAIMELEON project the Oncology and Radiology extensions are used, actively contributing within the OHDSI community to the evolution of these data models and extensions. Harmonized datasets across all clinical sites are essential for AI training. In order to deal with the heterogeneity of practices across institutions, we proceeded in three steps: -Radiologists defined the specific research question and inclusion/exclusion criteria for each use case and defined a list of variables to be collected for each type of cancer (lung, prostate, breast, colon, rectum) -The eCRFs were designed for each cancer.
EuCanImage data models Within the project the following data models are used for the various data types: -Non-imaging data. The ICGC (International Cancer Genome Consortium) ARGO (Accelerate Research in Genomic Oncology) dictionary was selected as a basis for the EuCanImage data model. It is a cancer-focused data model that describes the attributes and permissible values for all of the fields within the model. -Imaging data. Based upon MIABIS-2. 0 Core  [11, 12]  (Minimum Information About BIobank data Sharing) by BBMRI-ERIC  [13] , EuCanImage has adopted a similar model adapted for imaging metadata. This imaging model is a joint effort along with euCanSHare, a project for cardiac research from population cohorts in the EU and Canada. The metadata information is divided into three main levels, biobank, collection, and imaging datasets, where EuCanImage is displayed as a network of the biobank and/or collection.
The metadata information is divided into three main levels, biobank, collection, and imaging datasets. The model focuses on image acquisition parameters, extracted imaging biomarkers, and post-processing tools and has been extended to add disease type at the collection level using the International Classification of Diseases, Version 11 (ICD-11) ontology for classification of disease diagnosis. -EuCanImage/EIBIR catalog. The imaging catalog is built using the MOLGENIS  [14]  platform  [15]  (a modular web application for exploration of scientific data) and the user-search-interface is adapted from BBMRI-ERIC directory (molgenis-app-biobankexplorer) with the goal of future interoperability and sustainability. The user can search imaging metadata by network, country, the body part of interest, image modality, data types, collection types, and image access types.
EuCanImage: GDPR considerations and deidentification strategy Ensuring GDPR compliance principles is an ongoing effort in EuCanImage unfolding on three levels: (i) developing the policy framework for the platforms, (ii) creating data transfer and processing agreements governing data transactions, and (iii) translating legal and ethical requirements into technical ones and then implement these in the platform in a privacy by design fashion. More specifically, the following actions, tools, and measures will be implemented to ensure compliance with GDPR principles (art. 5): -Lawfulness, fairness, and transparency: Data access procedures, implemented in the EuCanImage catalogue, will include the collection of justification for data use. Data access procedures will be enforced for imaging, omics, and phenotypic data, in accordance with GDPR principles via business rules.
Through the catalog, each data provider will also guarantee the right to the information stated in arts 13 and 14 of the GDPR, which will be implemented as a binding term in data processing agreements. -Purpose limitation: The overall purpose for data use in EuCanImage is scientific. Each data provider (data controller) will implement this requirement (as per art. 6. 4 and 9. 2 j) or ask for the data subject's explicit consent for other purposes, where applicable. No secondary uses will be allowed without explicit consent. -Data minimization: The selection of data strictly is based on the minimal data needed for the AI/clinical tasks. -Accuracy, storage limitation, integrity, and confidentiality: Accuracy will be enforced starting with extensive data annotation and curation efforts. All patient-related personal data is removed within the clinic's local network before upload to the platform.
Patient health data as found in DICOM metadata is pseudonymized using SHA512/256. The pseudonymization key remains only visible to the source clinic. Associated consortium partners have no ability to reverse the pseudonymization. The pseudonymized data is guaranteed to be encrypted both in transit and at rest. Curators will check for, among other things, consistency and integrity in imaging and non-imaging data across sites. Revisions will focus on terms regarding the retention period, implementation of the right to be forgotten and other subjects' rights, related policies, tools, and procedures. -Accountability will be enforced primarily with the indication of data controllers, and type of controllership, at each step of the data flow as indicated in the data management plan and current architectural diagrams that are being reviewed.
INCISIVE INCISIVE aims to develop an interoperable pan-European federated repository of multimodal data sources, including imaging, biological, and other non-imaging clinical data is developed that enables the secure sharing of these data in compliance with ethical, legal and privacy demands, increasing accessibility to datasets, and enabling experimentation of AI-based solutions. INCI-SIVE adopts a hybrid federated architecture composed of both local data warehouses and a central repository with anonymized data. More details on the project architecture are provided in Additional file 1. Further, it includes various tools for curating the available data, and more specifically for image de-identification tools, quality control, and annotation tools. More details on the curation tools and approach for the project are provided in Additional file 1.
INCISIVE data models The INCISIVE CDM builds on the FHIR and DICOM standards. FHIR is a standard for healthcare data exchange, published by Health Level Seven (HL7). The terminology adopted for semantic interoperability is based on the SNOMED ontology, the most comprehensive, multilingual clinical terminology in the world, and LOINC a universal standard for identifying medical laboratory observations (Fig. 1 ). A list of variables to be collected for each type of cancer addressed (colorectal, lung, prostate, breast) was built based on inclusion and exclusion criteria agreed upon with clinical experts. These variables have been mapped to SNOMED ontology for clinical data and LOINC when needed. Cancer data is collected into a Microsoft Excel template according to the aforementioned mapping and then is fetched from an Extract-Transform-Load (ETL) tool which reads them and generates FHIR messages (xml messages).
ProCAncer-I ProCAncer-I aims to create ProstateNET, a scalable, sustainable, quality-controlled, prostate-related, medical imaging platform, focusing on answering a number of important prostate cancer relevant clinical questions. These clinical questions have been the drivers for defining a set of nine use cases, which enable the creation of a unique dataset in terms of data quantity, quality, and diversity. ProstateNET promotes multi-center interoperability via a large multiparametric (mp) magnetic resonance imaging (MRI) data repository alongside tools for data analysis and sharing. Large-scale data (clinical and imaging) and artificial intelligence (AI) algorithms co-exist in a centralized architecture storing completely anonymized data. More details on the project architecture are provided in Additional file 1.
ProCAncer-I: GDPR considerations and deidentification strategy The ProCAncer-I project only intends to process personal data at the clinical level. Once data is shared with the consortium and used for the development of the project tool, then all data will have been anonymized and the GDPR no longer applies. However, until the anonymization has been completed, the GDPR applies to the processing of personal data including the preanonymization stage and in performing the anonymization. The ProCAncer-I anonymization strategy follows a double stage anonymization process. In the first stage, each partner uses the software tool of preference under the condition that it will successfully remove/modify the number of personal health information (PHI) tags as defined by the DICOM guidelines. At this stage, the issue of DICOM list heterogeneity still exists but the datasets are successfully stripped out by any PHI containing tags.
PRIMAGE is currently focusing on the completion of this model, in particular by adding the missing entities and attributes related to the patient clinical variables, which were not yet defined in the first version, and by mapping these clinical variables to the OMOP CDM. Finally, the DICOM-MIABIS model is being used to store all the non-clinical information, this includes image preprocessing and annotation parameters, radiomics features, or DICOM metadata; however, for clinical data, the OMOP CDM is being used. The PRIMAGE database is natively built under a noSQL MongoDB database where all the information is stored in JSON format. Therefore, to map all the information into the proposed data models an ETL process is being developed.
PRIMAGE: GDPR considerations and deidentification strategy PRIMAGE deals with real-world data from NB and DIPG patients that have already been diagnosed and treated in different collaborating hospitals, registries, and trials to develop the in silico tumor behavior prediction models. In the project, access is granted to several registries and clinical trial databases for secondary use of available clinical data. The databases included are the SIOPEN-r-net (International Society of Pediatric Oncology European Neuroblastoma Research Network) and the GPOH clinical trials database (German Society of Pediatric Oncology and Hematology). The secondary use of clinical trial data must necessarily be done in agreement with the Ethics Committee and highly respecting data protection rules. For the external validation of the developed algorithms, other hospitals will collaborate with PRIMAGE sharing their data.
The ethics committee approval of these external collaborators is mandatory. Data from the SIOPEN-r-net database was already pseudonymized using EUPID (European Unified Patient Identity)  [19]. To ease the linkage between external databases and the PRIMAGE database EUPID was also included as the pseudonymization tool in the PRIMAGE project. As detailed in the previous section, data comes from different sources following different data upload pipelines. Regardless of the source of the data, when a new patient is incorporated in the PRIMAGE database, a new and unique pseudonym is given for its pseudonymization. When uploading an associated imaging study, this pseudonym is used to substitute personal data in the DICOM files such as the patient name or the patient ID and all the DICOM tags with sensitive information as stated in the DICOM standards PS3.
Architecture Two projects (ProCAncer-I and PRIMAGE) opted for a centralized architecture. CHAIMELEON and EuCan-Image's architecture is hybrid, and INCISIVE is mainly federated, although now moving towards a hybrid approach. Centralizing data provides efficiency in data exploration and processing, while a federated approach provides data holders with additional control and reassurance over data privacy and data sovereignty. Taking into account the advantages and disadvantages of the different architectures, a hybrid approach seems to be the best one, combining the advantages of both centralized and federated architectures. In a hybrid approach, data is centralized only when required after data sharing has been authorized by the data providers. This approach allows combining the ability to have very large data collections locally stored in data warehouses at sites and the ability to make use of real-time cloud high-performance resources for specific uses and specific datasets.
Data models and standards While DICOM is a universal standard for medical imaging and was adopted by all projects, clinical data suffer from greater variability. Several ontologies or data models coexist and projects combine the OMOP-CDM, SNOMED-CT, HL7 FHIR, or ICGC-ARGO. Multiple terminologies are used to further homogenize various fields. It is also worth mentioning that although the objectives of each project were close, each project defined the clinical variables they wanted to collect and each project developed its eCRF. Nevertheless, all projects work on existing current terminologies and standards to fill the gaps for medical imaging. Metadata used to describe a dataset also varies from project to project, although all projects refer to the MIABIS recommendations and the MIABIS-DICOM proposition.
GDPR considerations There is no "one-size-fits-all" solution to ensuring the appropriate legal basis for the processing of health data for scientific research. All projects have appointed a Data Protection Officer; have determined the roles of the different stakeholders; have formalized arrangements such as joint controller agreements data processing agreements and data transfer agreements; have run a data protection impact assessment (DPIA); and have ensured that strict privacy guarantees are in place. Some aspects may differ however from project to project and some GDPR aspects may go against some objectives of the projects. Below are some specifics that have led to discussions within the projects, calling for the need to have clear guidelines.
Accountability While data protection impact assessments (DPIA) allow to achieve baseline compliance and drive a discussion on the discovered risks to privacy and means to mitigate them, observing the principle of accountability in large consortia is difficult, as there are many different stakeholders which take responsibility for various aspects of processing. The new legal frameworks such as the Data Governance Act and the European Health Data Space will be of great help to set up data governance structures, based on an in-depth mapping of the roles of the stakeholders (data providers, data users, and infrastructure administrators) and careful balancing of their interests and liabilities.
Deidentification While all projects have adopted strict de-identification measures, based on the confidentiality profile defined by the DICOM Standard, and have taken both technical and organizational measures so that data users cannot reidentify patients, there are significant differences in approaches. Some projects have opted for pseudonymization, while others perform anonymization. In pseudonymization, the data provider keeps the key to the patients' original identity. This has several advantages: -It facilitates the curation process where authorized users can access the EHR data to verify the data completeness and address any doubts when dealing with outliers or incoherent data -Additional time points can be added to the data and are linked with previously collected information -Patient withdrawals can be handled -Patients can be reached by caregivers in case of incidental findings in a research project.
In anonymization, deidentification is irreversible. No table of correspondence is kept, even at the hospital level, so it is no longer possible to identify a person. Anonymous data is no longer considered personal data. Therefore, anonymous data falls outside of the scope of GDPR. For this reason, anonymization may be preferred by some institutions, as otherwise, it would be difficult for them to assume any responsibility over personal data that are stored in an environment that is not managed by them. Anonymization, however, requires specific precautions: -As it is not possible to add timepoints, only full cases with longitudinal information and evaluation during full follow-up period can be sent -Specific measures need to be taken to ensure no duplicates of patients are sent -The European Data Protection Board (EDPB) has warned that anonymization is difficult to achieve and maintain over time "taking into consideration the available technology at the time of the processing and technological developments".
Conclusions and perspectives Developing high-quality AI models for health imaging requires access to large amounts of curated, annotated, and harmonized imaging data along with their metadata and associated clinical labeling information. However, these datasets might have been produced by different vendors and protocols and use different terminologies and data models to be represented. As such, appropriate infrastructures are required for enabling access to the data and raising the trust of data providers in data sharing initiatives. Various approaches have been implemented already by the projects participating in the AI4HI initiative, ranging from centrally collecting anonymized data to federated learning performed at each clinical center. Independent of the approach, a harmonization methodology is also required to ensure homogeneous access to heterogeneous data.
To further harmonize practices, contribute to standards, ensure interoperability and in-fine, raise trust in data sharing, and advance AI development in cancer imaging, the five projects of the AI4HI initiative have gathered in a new project: EuCAIM (European Federation for Cancer Images) funded under the Digital Europe Programme. This very ambitious project, composed of 79 partners, will demonstrate technologies, operational procedures, and legal frameworks which are reproducible, efficient, and cost-effective for enabling a cross-border reuse of health data in research and innovation projects.
Acknowledgements This paper has been supported by the  H2020 Artificial Intelligence for Health Imaging  ( AI4HI ) initiative and more specifically by the Data Storage/Curation/ Management working group. The AI4HI working group is formulated by five  EU projects on Artificial Intelligence for Medical Imaging , the EuCanImage, INCISIVE, ProCAncer-I, CHAIMELEON, and PRIMAGE that have received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreements No. 952103 ,  952179 ,  952159 ,  952172 , and  826494 , respectively.