TY - JOUR A1 - Nan, Yang A1 - Del Ser, Javier A1 - Walsh, Simon A1 - Schönlieb, Carola A1 - Roberts, Michael A1 - Selby, Ian A1 - Howard, Kit A1 - Owen, John A1 - Neville, Jon A1 - Guiot, Julien A1 - Ernst, Benoit A1 - Pastor, Ana A1 - Alberich-Bayarri, Angel A1 - Menzel, Marion Irene A1 - Walsh, Sean A1 - Vos, Wim A1 - Flerin, Nina A1 - Charbonnier, Jean-Paul A1 - Rikxoort, Eva van A1 - Chatterjee, Avishek A1 - Woodruff, Henry A1 - Lambin, Philippe A1 - Cerdá-Alberich, Leonor A1 - Martí-Bonmatí, Luis A1 - Herrera, Francisco A1 - Yang, Guang T1 - Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions JF - Information Fusion N2 - Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research UR - https://doi.org/10.1016/j.inffus.2022.01.001 KW - Information fusion KW - data harmonisation KW - data standardisation KW - domain adaptation KW - reproducibility Y1 - 2022 UR - https://doi.org/10.1016/j.inffus.2022.01.001 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13845 SN - 1566-2535 VL - 2022 IS - 82 SP - 99 EP - 122 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Martí-Bonmatí, Luis A1 - Miguel, Ana A1 - Suárez, Amelia A1 - Aznar, Mario A1 - Beregi, Jean Paul A1 - Fournier, Laure A1 - Neri, Emanuele A1 - Laghi, Andrea A1 - França, Manuela A1 - Sardanelli, Francesco A1 - Penzkofer, Tobias A1 - Lambin, Philippe A1 - Blanquer, Ignacio A1 - Menzel, Marion Irene A1 - Seymour, Karine A1 - Figueiras, Sergio A1 - Krischak, Katharina A1 - Martínez, Ricard A1 - Mirsky, Yisroel A1 - Yang, Guang A1 - Alberich-Bayarri, Ángel T1 - CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools JF - Frontiers in oncology N2 - The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool 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. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology. UR - https://doi.org/10.3389/fonc.2022.742701 KW - radiology KW - artificial intelligence-AI KW - cancer imaging KW - cancer management KW - quantitative imaging biomarkers KW - image harmonization Y1 - 2022 UR - https://doi.org/10.3389/fonc.2022.742701 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28156 SN - 2234-943X VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - INPR A1 - Nan, Yang A1 - Del Ser, Javier A1 - Walsh, Simon A1 - Schönlieb, Carola A1 - Roberts, Michael A1 - Selby, Ian A1 - Howard, Kit A1 - Owen, John A1 - Neville, Jon A1 - Guiot, Julien A1 - Ernst, Benoit A1 - Pastor, Ana A1 - Alberich-Bayarri, Angel A1 - Menzel, Marion Irene A1 - Walsh, Sean A1 - Vos, Wim A1 - Flerin, Nina A1 - Charbonnier, Jean-Paul A1 - Rikxoort, Eva van A1 - Chatterjee, Avishek A1 - Woodruff, Henry A1 - Lambin, Philippe A1 - Cerdá-Alberich, Leonor A1 - Martí-Bonmatí, Luis A1 - Herrera, Francisco A1 - Yang, Guang T1 - Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions N2 - Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research. UR - https://doi.org/10.48550/arXiv.2201.06505 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2201.06505 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46433 PB - arXiv CY - Ithaca ER -