An AI Platform integrating imaging data and models, supporting precision care through prostate cancer’s continuum

Risultati finali

Deep Learning Master model and Radiomic Signatures
2nd Communication and Dissemination Activities Report
Retrospective Data Retrieval and upload
Explainable, interpretable, ethical and trustworthy AI framework

Explainable interpretable ethical and trustworthy AI framework

Imaging Repository, HPC, tools and services

Imaging Repository HPC tools and services

Vendor-specific AI models

Vendorspecific AI models

Report on requirements elicitation and initial design of the platform
Pre-processing tools

Preprocessing tools

Data Curation, Annotation and Upload tools

Data Curation Annotation and Upload tools

Initial Data Management Plan
FAIRification of radiomics and AI
Final Data Management Plan
1st Communication and Dissemination Activities Report

Open Research Data Pilot

Final version of the ProCAncer-I platform, interoperability and AI services

Pubblicazioni

PROper-Net: A Deep-Learning Approach for Prostate’s Peripheral Zone Segmentation based on MR imaging

Autori: E. Mylona, D. Zaridis, N. Tachos, K. Marias, M. Tsiknakis and D. I. Fotiadis
Pubblicato in: IEEE 21st Mediterranean Electrotechnical Conference (MELECON), 2022, Pagina/e pp. 1124-1128
Editore: IEEE
DOI: 10.1109/melecon53508.2022.9843082

Exploring the potential and challenges of AI in clinical diagnostics and remote assistance of individuals

Autori: Andrea Berti, Rossana Buongiorno, Gianluca Carloni, Claudia Caudai, Giulio Del Corso, Danila Germanese, Eva Pachetti, Maria Antonietta Pascali and Sara Colantonio
Pubblicato in: Convegno Nazionale CINI sull'Intelligenza Artificiale (ITAL-IA) , Pisa, Italy, 29-31 May (Session Workshop on AI for health and wellbeing), Numero 16/09/2023, 2023
Editore: Zenodo
DOI: 10.5281/zenodo.8005500

Fine-tuned feature selection to improve prostate segmentation via a fully connected meta-learner architecture

Autori: D. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis and D. I. Fotiadis
Pubblicato in: IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2022, Pagina/e pp. 01-04, ISSN 2641-3604
Editore: IEEE-EMBS
DOI: 10.1109/bhi56158.2022.9926929

Comparison of Machine and Deep Learning models for automatic segmentation of prostate cancers on multiparametric MRI

Autori: Giovanni Maimone; Giulia Nicoletti; Simone Mazzetti; Daniele Regge; Valentina Giannini
Pubblicato in: Crossref, Numero 10, 2022, ISBN 978-1-6654-8299-8
Editore: IEEE
DOI: 10.1109/memea54994.2022.9856530

Data Ingestion for AI in Prostate Cancer

Autori: Haridimos Kondylakis , Stelios Sfakianakis , Varvara Kalokyri , Nikolaos Tachos , Dimitrios Fotiadis , Kostas Marias , Manolis Tsiknakis
Pubblicato in: Stud Health Technol Inform . 2022 May 25;294:244-248., Numero Stud Health Technol Inform . 2022 May 25;294:244-248., 2022, ISSN 1879-8365
Editore: PUBMED
DOI: 10.3233/shti220446

Bridging gaps between images and data: a systematic update on imaging biobanks

Autori: Michela Gabelloni, Lorenzo Faggioni, Rita Borgheresi, Giuliana Restante, Jorge Shortrede, Lorenzo Tumminello, Camilla Scapicchio, Francesca Coppola, Dania Cioni, Ignacio Gómez-Rico, Luis Martí-Bonmatí & Emanuele Neri
Pubblicato in: Imaging Informatics and Artificial Intelligence, Numero Published: 10 January 2022, 2022
Editore: SpringerLink
DOI: 10.1007/s00330-021-08431-6

Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI

Autori: A. Saha, M. Hosseinzadeh, H. Huisman
Pubblicato in: 34th Conference on Neural Information Processing Systems (NeurIPS), 2020
Editore: Medical Imaging Meets NeurIPS Workshop – 34th Conference on Neural Information Processing Systems (NeurIPS)

On the Effectiveness of 3D Vision Transformers for the Prediction of Prostate Cancer Aggressiveness

Autori: Eva Pachetti, Sara Colantonio & Maria Antonietta Pascali
Pubblicato in: """Image Analysis and Processing. ICIAP 2022 Workshops, Lecture Notes in Computer Science", Numero vol 13374. Springer, 2022, ISBN 978-3-031-13324-4
Editore: SPRINGER
DOI: 10.1007/978-3-031-13324-4_27

A Deep Learning-based cropping technique to improve segmentation of prostate's peripheral zone

Autori: Zaridis Dimitris; Mylona Eugenia; Tachos Nikolaos; Marias Kostas; Tsiknakis Manolis; Fotiadis Dimitrios
Pubblicato in: Numero 5, 2021
Editore: 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)
DOI: 10.1109/BIBE52308.2021.9635576

Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI —Should Different Clinical Objectives Mandate Different Loss Functions?

Autori: Anindo Saha, Joeran Bosma, Jasper Linmans, Matin Hosseinzadeh, Henkjan Huisman
Pubblicato in: 35th Conference on Neural Information Processing Systems (NeurIPS), 2021
Editore: Medical Imaging Meets NeurIPS Workshop - 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
DOI: 10.48550/arxiv.2110.12889

Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning–based Prostate Cancer Detection Using Biparametric MRI

Autori: Joeran S. Bosma, Anindo Saha, Matin Hosseinzadeh, Ivan Slootweg, Maarten de Rooij, and Henkjan Huisman
Pubblicato in: Radiology: Artificial Intelligence 2023 5:5, Numero Vol. 5, No. 5, 2023, ISSN 2638-6100
Editore: Radiology: Artificial Intelligence
DOI: 10.1148/ryai.230031

A first look into radiomics application in testicular imaging: A systematic review

Autori: Fanni, Salvatore C. and Febi, Maria and Colligiani, Leonardo and Volpi, Federica and Ambrosini, Ilaria and Tumminello, Lorenzo and Aghakhanyan, Gayane and Aringhieri, Giacomo and Cioni, Dania and Neri, Emanuele
Pubblicato in: Front. Radiol., 17 April 2023., 2023, ISSN 2673-8740
Editore: Frontiers in Radiology
DOI: 10.3389/fradi.2023.1141499

A deep look into radiomics

Autori: Camilla Scapicchio · Michela Gabelloni· Andrea Barucci · Dania Cioni · Luca Saba · Emanuele Neri
Pubblicato in: La radiologia medica volume 126, (2021), 2021, Pagina/e 1296–1311, ISSN 1826-6983
Editore: SPRINGER
DOI: 10.1007/s11547-021-01389-x

a report on the experiences of five EU projects

Autori: Haridimos Kondylakis; Varvara Kalokyri; Stelios Sfakianakis; Kostas Marias; Manolis Tsiknakis; Ana Jimenez-Pastor; Eduardo Camacho-Ramos; Ignacio Blanquer; J. Damian Segrelles; Sergio López-Huguet; Caroline Barelle; Magdalena Kogut-Czarkowska; Gianna Tsakou; Nikolaos Siopis; Zisis Sakellariou; Paschalis Bizopoulos; Vicky Drossou; Antonios Lalas; Konstantinos Votis; Pedro Mallol; Luis Marti-Bonmat
Pubblicato in: European radiology experimental, 7(1):20. Springer Open, Numero 24, 2023, ISSN 2509-9280
Editore: Springer
DOI: 10.1186/s41747-023-00336-x

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 & Gianna Tsakou
Pubblicato in: Insights into Imaging volume 13, Article number: 89 (2022), 2022, ISSN 1869-4101
Editore: Springer Science and Business Media Deutschland GmbH
DOI: 10.1186/s13244-022-01220-9

A smart cropping pipeline to improve prostate's peripheral zone segmentation on MRI using Deep Learning

Autori: Zaridis Dimitris; Mylona Eugenia; Tachos Nikolaos; Marias Kostas; Tsiknakis Manolis; Fotiadis Dimitrios
Pubblicato in: EAI Endorsed Transactions on Bioengineering and Bioinformatics, Numero 2, 2022, ISSN 2709-4111
Editore: EUDL
DOI: 10.4108/eai.24-2-2022.173546

Virtual biopsy in abdominal pathology: where do we stand?

Autori: Arianna Defeudis, Jovana Panic, Giulia Nicoletti, Simone Mazzetti, Valentina Giannini and Daniele Regge
Pubblicato in: Volume 5, Numero 1November 2023, 2023, ISSN 2513-9878
Editore: BJR OPEN /British Institute of Radiology
DOI: 10.1259/bjro.20220055

Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review

Autori: Avtantil Dimitriadis, Eleftherios Trivizakis, Nikolaos Papanikolaou, Manolis Tsiknakis & Kostas Marias
Pubblicato in: Insights into Imaging 13(188), 2022, ISSN 1869-4101
Editore: Springer Science and Business Media Deutschland GmbH
DOI: 10.1186/s13244-022-01315-3

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

Autori: Kondylakis, Haridimos; Ciarrocchi, Esther; Cerda-Alberich, Leonor; Chouvarda, Ioanna; Fromont, Lauren A.; Garcia-Aznar, Jose Manuel; Kalokyri, Varvara; Kosvyra, Alexandra; Walker, Dawn; Yang, Guang; Neri, Emanuele; The AI4HealthImaging Working Group on metadata models
Pubblicato in: Eur Radiol Exp 6, 29 (2022), Numero 24, 2022, ISSN 2509-9280
Editore: EUROPEAN RADIOLOGY
DOI: 10.1186/s41747-022-00281-1

Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Autori: Valentina Giannini; Simone Mazzetti; Giovanni Cappello; Valeria Maria Doronzio; Lorenzo Vassallo; Filippo Russo; Alessandro Giacobbe; Giovanni Muto; Daniele Regge
Pubblicato in: Diagnostics (Basel), Numero 2021 May 28;11(6):973, 2021, Pagina/e 973, ISSN 2075-4418
Editore: pubmed
DOI: 10.3390/diagnostics11060973

A segmentation-based method improving the performance of N4 bias field correction on T2weighted MR imaging data of the prostate

Autori: Aikaterini Dovrou and Katerina Nikiforaki and Dimitris Zaridis and Georgios C. Manikis and Eugenia Mylona and Nikolaos Tachos and Manolis Tsiknakis and Dimitrios I. Fotiadis and Kostas Marias
Pubblicato in: Magnetic Resonance Imaging Volume 101, September 2023,, 2023, Pagina/e Pages 1-12, ISSN 0730-725X
Editore: Elsevier BV
DOI: 10.1016/j.mri.2023.03.012

"""Discrimination of Tumor Texture Based on MRI Radiomic Features: Is There a Volume Threshold? A Phantom Study"

Autori: João Santinha, Linda Bianchini, Mário Figueiredo, Celso Matos, Alessandro Lascialfari, Nikolaos Papanikolaou, Marta Cremonesi, Barbara A. Jereczek-Fossa, Francesca Botta and Daniela Origgi (
Pubblicato in: MDPI in Applied Sciences Applied Sciences , Volume 12, 2022, ISSN 2076-3417
Editore: MDPI
DOI: 10.3390/app12115465

Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics

Autori: Ana Rodrigues, João Santinha, Bernardo Galvão, Celso Matos, Francisco M. Couto and Nickolas Papanikolaou
Pubblicato in: Cancers 2021, Numero Volume 13 Numero 23,, 2021, Pagina/e 6065, ISSN 2072-6694
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers13236065

3D-Vision-transformer stacking ensemble for assessing prostate cancer aggressiveness from T2w images

Autori: Pachetti E.; Colantonio S.
Pubblicato in: Bioengineering (Basel) 10 (2023). doi:10.3390/bioengineering10091015, Numero Special Numero Clinical Diagnosis and Treatment Inspired by Artificial Intelligence, 2022, ISSN 2306-5354
Editore: ternational Conference on Image Analysis and Processing
DOI: 10.3390/bioengineering10091015

End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction.

Autori: Anindo Saha; Matin Hosseinzadeh; Henkjan J. Huisman
Pubblicato in: Medical Image Analysis, Numero Volume 73, October 2021, 102155, 2021, ISSN 1361-8415
Editore: Elsevier BV
DOI: 10.1016/j.media.2021.102155

Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness

Autori: Ana Rodrigues; Nuno Rodrigues; João Santinha; Maria V. Lisitskaya; Aycan Uysal; Celso Matos; Inês Domingues; Nickolas Papanikolaou
Pubblicato in: Crossref, Numero 9, 2023, ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.1038/s41598-023-33339-0

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.

Autori: Jasper J. Twilt; Kicky G. van Leeuwen; Henkjan J. Huisman; Jurgen J. Fütterer; Maarten de Rooij
Pubblicato in: Diagnostics 2021, 11(6), 959, Numero 2021 May 26, 2021, ISSN 2075-4418
Editore: MDPI
DOI: 10.3390/diagnostics11060959

A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI

Autori: Nuno M. Rodrigues; Sara Silva; Leonardo Vanneschi; Nickolas Papanikolaou
Pubblicato in: Crossref, Numero Cancers 2023, 15(5), 1467;, 2023, ISSN 2072-6694
Editore: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers15051467

A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation

Autori: Valentina Giannini, Simone Mazzetti, Arianna Defeudis, Giuseppe Stranieri, Marco Calandri, Enrico Bollito, Martino Bosco, Francesco Porpiglia, Matteo Manfredi, Agostino De Pascale, Andrea Veltri, Filippo Russo and Daniele Regge
Pubblicato in: Front. Oncol., 01 October 2021, 2021, ISSN 2234-943X
Editore: Frontiers Media S. A.
DOI: 10.3389/fonc.2021.718155

Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones

Autori: Dimitrios I. Zaridis; Eugenia Mylona; Nikolaos Tachos; Vasileios C. Pezoulas; Grigorios Grigoriadis; Nikos Tsiknakis; Kostas Marias; Manolis Tsiknakis; Dimitrios I. Fotiadis
Pubblicato in: Scientific Reports, 13, 714 (2023), Numero 19, 2023, ISSN 0730-725X
Editore: Elsevier BV
DOI: 10.1038/s41598-023-27671-8

The Design of Trustworthy AI System: a Deep Look into the Transparency of Data, Models, and Decisions

Autori: Sara Colantonio
Pubblicato in: IEEE EMBC, Numero 17, 2022
Editore: Mini-Symposium on Trustworthy AI in Cancer Imaging Research, within IEEE EMBC
DOI: 10.5281/zenodo.7181554

Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)

Autori: Saha, Anindo; Twilt, Jasper Jonathan; Bosma, Joeran Sander; van Ginneken, Bram; Yakar, Derya; Elschot, Mattijs; Veltman, Jeroen; Fütterer, Jurgen; de Rooij, Maarten; Huisman, Henkjan
Pubblicato in: Numero 1, 2022
Editore: Zenodo
DOI: 10.5281/zenodo.6522364

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; Aussó, Susanna; Alberich, Leonor Cerdá; Marias, Kostas; Tsiknakis, Manolis; Colantonio, Sara; Papanikolaou, Nickolas; Salahuddin, Zohaib; Woodruff, Henry C; Lambin, Philippe; Martí-Bonmatí, Luis
Pubblicato in: Numero 3, 2021
Editore: arxiv.org

Artificial Intelligence for precision medicine

Autori: Sara Colantonio
Pubblicato in: Numero 4, 2022
Editore: ZENODO
DOI: 10.5281/zenodo.7181429

Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues

Autori: Albertoni, Riccardo; Colantonio, Sara; Skrzypczyński, Piotr; Stefanowski, Jerzy
Pubblicato in: Computer Science > Artificial Intelligence, Numero 23, 2023
Editore: eprint arXiv
DOI: 10.48550/arxiv.2302.12691

A new smart-cropping pipeline for prostate segmentation using deep learning networks

Autori: Zaridis Dimitris; Mylona Eugenia; Tachos Nikolaos; Marias Kostas; Papanikolaou Nikolaos; Tsiknakis Manolis; Fotiadis Dimitrios
Pubblicato in: Numero 4, 2021
Editore: arxiv.org
DOI: 10.48550/arXiv.2107.02476

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