A multimodal AI-based toolbox and an interoperable health imaging repository for the empowerment of imaging analysis related to the diagnosis, prediction and follow-up of cancer

Risultati finali

Report on the dissemination activities carried out during the first reporting period

Clustering Events Proceedings and Raising Awareness Campaigns Results V1

Proceedings of the clustering events including relevant material eg presentations etc along with the strategy presentation and results of the campaign regarding the panEuropean repository of health images

Second Dissemination Activities Report

Report on the dissemination activities carried out during the second reporting period

INCISIVE Web Presence

Description of the project website and social media accounts.

Initial Data Management Plan

This report will describe the research data generated in the project and how they will be published In particular it will include 1 the data and data format 2 Metadata content and format 3 Policies for access sharing and reuse and 4 Longterm storage and data management

Pubblicazioni

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

Autori: ESTHER CIARROCCHI; Emanuele Neri
Pubblicato in: European Radiology Experimental, Numero 25099280, 2022, ISSN 2509-9280
Editore: Springer
DOI: 10.1186/s41747-022-00281-1

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability

Autori: Jamalzadeh S, Häkkinen A, Andersson N, Huhtinen K, Laury A, Hietanen S, Hynninen J, Oikkonen J, Carpén O, Virtanen A, Hautaniemi S
Pubblicato in: Laboratory Investigation, Numero February 2022, 2022, ISSN 1530-0307
Editore: Springer Nature
DOI: 10.1038/s41374-022-00743-5

The Holistic Perspective of the INCISIVE Project—Artificial Intelligence in Screening Mammography.

Autori: Lazic I, Agullo F, Ausso S, Alves B, Barelle C, Berral JL, Bizopoulos P, Bunduc O, Chouvarda I, Dominguez D, Filos D, Gutierrez-Torre A, Hesso I, Jakovljević N, Kayyali R, Kogut-Czarkowska M, Kosvyra A, Lalas A, Lavdaniti M, Loncar-Turukalo T, Martinez-Alabart S, Michas N, Nabhani-Gebara S, Raptopoulos A, Roussakis Y, Stalika E, Symvoulidis C, Tsave O, Votis K, Charalambous A.
Pubblicato in: Applied Sciences, Numero 20763417, 2022, ISSN 2076-3417
Editore: MDPI
DOI: 10.3390/app12178755

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Autori: Kondylakis, H., Kalokyri, V., Sfakianakis, S., Marias, K., Tsiknakis, M., Jimenez-Pastor, A., Camacho-Ramos, E., Blanquer, I., Segrelles, J. D., López-Huguet, S., Barelle, C., Kogut-Czarkowska, M., Tsakou, G., Siopis, N., Sakellariou, Z., Bizopoulos, P., Drossou, V., Lalas, A., Votis, K., Mallol, P., … Lekadir, K.
Pubblicato in: Eur Radiol Exp, 2023, ISSN 2509-9280
Editore: SpringerOpen
DOI: 10.1186/s41747-023-00336-x

Experiences of cancer survivors in Europe: Has anything changed? Can artificial intelligence offer a solution?

Autori: Iman Hesso; Reem Kayyali; Andreas Charalambous; Maria Lavdaniti; Evangelia Stalika; Maria Lelegianni; Shereen Nabhani-Gebara
Pubblicato in: Frontiers in Oncology, Numero 2234943X, 2022, ISSN 2234-943X
Editore: Frontiers Media S. A.
DOI: 10.3389/fonc.2022.888938

A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging

Autori: Protonotarios NE, Katsamenis I, Sykiotis S, Dikaios N, Kastis GA, Chatziioannou SN, Metaxas M, Doulamis N, Doulamis A.
Pubblicato in: Biomed Phys Eng Express, Numero February 2022, 2022, ISSN 2057-1976
Editore: Pub.Med.gov
DOI: 10.1088/2057-1976/ac53bd

Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper

Autori: Luis Marti‑Bonmati, Dow‑Mu Koh, Katrine Riklund, Maciej Bobowicz, Yiannis Roussakis, Joan C. Vilanova, Jurgen J. Fütterer, Jordi Rimola, Pedro Mallol, Gloria Ribas, Ana Miguel, Manolis Tsiknakis, Karim Lekadir and Gianna Tsakou
Pubblicato in: Insights into Imaging, Numero 2022, 2022, ISSN 1869-4101
Editore: Springer Science and Business Media Deutschland GmbH
DOI: 10.1186/s13244-022-01220-9

H&E image analysis pipeline for quantifying morphological features

Autori: Valeria, Ariotta; Oskari, Lehtonen; Shams, Salloum; Giulia, Micoli; Kari, Lavikka; Ville, Rantanen; Johanna, Hynninen; Anni, Virtanen; Sampsa, Hautaniemi
Pubblicato in: Journal of Pathology Informatics, Vol 14, Iss , Pp 100339- (2023), Numero 1, 2023, ISSN 2153-3539
Editore: Elsevier
DOI: 10.1016/j.jpi.2023.100339

Is Elevated Choline on Magnetic Resonance Spectroscopy a Reliable Marker of Breast Lesion Malignancy?

Autori: Prvulovic Bunovic N, Sveljo O, Kozic D, Boban J.
Pubblicato in: Frontiers in Oncology, Numero September 2021, 2021, ISSN 2234-943X
Editore: Frontiers Media S. A.
DOI: 10.3389/fonc.2021.610354

Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms

Autori: Ioannis N. Tzortzis; Agapi Davradou; Ioannis Rallis; Maria Kaselimi; Konstantinos Makantasis; Anastasios Doulamis; Nikolaos Doulamis
Pubblicato in: Diagnostics, Vol 12, Iss 10, p 2389 (2022), Numero 1, 2022, ISSN 2075-4418
Editore: Diagnosis
DOI: 10.3390/diagnostics12102389

Information extraction from clinical records: an example for breast cancer

Autori: I. Lazic, N. Jakovljevic, J. Boban, I. Nosek and T. Loncar-Turukalo
Pubblicato in: IEEE 21st Mediterranean Electrotechnical Conference (MELECON), 2022
Editore: IEEE Xplore
DOI: 10.1109/melecon53508.2022.9842995

CN9 Mapping the Functional Assessment of Cancer Therapy (FACT-G) in Greek patients with neoplasm: An interplay of statistical and bioinformatics approach

Autori: E. Stalika, K. Gavrilaki, I. Koziokos, I. Chouvarda, M. Lavdaniti
Pubblicato in: Annals of Oncology, 2022
Editore: Elsevier
DOI: 10.1016/j.annonc.2022.07.319

A deep-learning based diagnostic framework for Breast Cancer.

Autori: Stavros Sykiotis, Ioannis Tzortzis, Aikaterini Angeli, Nikolaos Doulamis, and Dimitrios Kalogeras.
Pubblicato in: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '22), 2022
Editore: Association for Computing Machinery
DOI: 10.1145/3529190.3534769

Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation

Autori: Berral JL, Aranda O, Dominguez JL, Torres J.
Pubblicato in: IPDPS 36th IEEE International Parallel & Distributed Processing Symposium, Workshop on Scalable Deep Learning (ScaDL)- IEEE Xplore, Numero October 2021 (Arxiv), May 2022 (Conference), 2021
Editore: IEEExplore Library
DOI: 10.48550/arxiv.2110.15884

Towards Data Integration for AI in Cancer Research

Autori: Kosvyra A, Filos D, Fotopoulos D, Olga T and Chouvarda I.
Pubblicato in: Annu Int Conf IEEE Eng Med Biol Soc, Numero November 2021, 2021
Editore: Pub.Med.gov
DOI: 10.1109/embc46164.2021.9629675

Towards Lung Cancer Staging via Μultipositional Radiomics and Machine Learning

Autori: Fotopoulos, D, Filos D, Xinou E, and Chouvarda I
Pubblicato in: In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies, 2023, ISBN 978-989-758-631-6
Editore: BIOSIGNALS
DOI: 10.5220/0011781500003414

Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool

Autori: Alexandra Kosvyra; Dimitrios Filos; Dimitrios Fotopoulos; Olga Tsave; Ioanna Chouvarda
Pubblicato in: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022
Editore: IEEE Xplore
DOI: 10.1109/embc48229.2022.9871018

Combining Machine Learning and Network Analysis Pipelines: The Case of Microbiome and Metabolomics Data in Colorectal Cancer

Autori: Ntzioni E, Chouvarda I.
Pubblicato in: Stud Health Technol Inform, 2022
Editore: IOS Press
DOI: 10.3233/shti210965

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Autori: Lekadir, Karim; Osuala, Richard; Gallin, Catherine; Lazrak, Noussair; Kushibar, Kaisar; Tsakou, Gianna; Auss��, Susanna; Alberich, Leonor Cerd��; Marias, Kostas; Tsiknakis, Manolis; Colantonio, Sara; Papanikolaou, Nickolas; Salahuddin, Zohaib; Woodruff, Henry C; Lambin, Philippe; Mart��-Bonmat��, Luis
Pubblicato in: arXiv, 2021
Editore: arXiv
DOI: 10.48550/arxiv.2109.09658

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Autori: Lekadir, Karim; Feragen, Aasa; Fofanah, Abdul Joseph; Frangi, Alejandro F; Buyx, Alena; Emelie, Anais; Lara, Andrea; Porras, Antonio R; Chan, An-Wen; Navarro, Arcadi; Glocker, Ben; Botwe, Benard O; Khanal, Bishesh; Beger, Brigit; Wu, Carol C; Cintas, Celia; Langlotz, Curtis P; Rueckert, Daniel; Mzurikwao, Deogratias; Fotiadis, Dimitrios I; Zhussupov, Doszhan; Ferrante, Enzo; Meijering, Erik; Weick
Pubblicato in: arXiv, Numero 1, 2023, ISSN 2331-8422
Editore: Cornell University
DOI: 10.48550/arxiv.2309.12325

Pseudonymization and anonymization of personal data in scientific research projects

Autori: Pseudonymization and anonymization of personal data in scientific research projects
Pubblicato in: KWARTALNIK PRAWO NOWYCH TECHNOLOGII, Numero July 2021, 2021, ISSN 2720-1600
Editore: C.H. Beck

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