2024

Alessa Hering, Sarah de Boer, Anindo Saha, Jasper J. Twilt, Derya Yakar, Maarten de Rooij, Henkjan Huisman, Joeran S. Bosma, Deformable MRI Sequence Registration for AI-based Prostate Cancer Diagnosis. arXiv, arXiv:2404.09666., https://doi.org/10.48550/arXiv.2404.09666

 

Pachetti, E., Tsaftaris, S. A., and Colantonio, S., Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification. arXiv, arXiv:2403.17530., https://doi.org/10.48550/arXiv.2403.17530

 

Nuno Miguel Rodrigues, José Guilherme de Almeida, Ana Sofia Castro Verde, Ana Mascarenhas Gaivão, Carlos Bilreiro, Inês Santiago, Joana Ip, Sara Belião, Raquel Moreno, Celso Matos, Leonardo Vanneschi, Manolis Tsiknakis, Kostas Marias, Daniele Regge, Sara Silva, Nickolas Papanikolaou, (2024). Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data. Computers in Biology and Medicine, Volume 171, https://doi.org/10.1016/j.compbiomed.2024.108216.

 

2023

Anwana, T.O., Barud, K., Cepic, M., Johnson, E., Königseder, M., Wagner, MC. (2024). Consent and Retrospective Data Collection. In: Corrales Compagnucci, M., Minssen, T., Fenwick, M., Aboy, M., Liddell, K. (eds) The Law and Ethics of Data Sharing in Health Sciences. Perspectives in Law, Business and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-99-6540-3_7

 

Varvara Kalokyri, Haridimos Kondylakis, Stelios Sfakianakis, Katerina Nikiforaki, Ioannis Karatzanis, Simone Mazzetti, Nikolaos Tachos, Daniele Regge, Dimitrios I. Fotiadis, Konstantinos Marias, Manolis Tsiknakis, “MI-CDM: Extending OMOP-CDM for registering Medical Imaging metadata and subsequent curation processes,”,JCO Clinical Cancer Informatics, Vol. 7, Data Architecture and Models 10.1200/CCI.23.00101

 

Joeran S. Bosma , Anindo Saha, Matin Hosseinzadeh, Ivan Slootweg, Maarten de Rooij, Henkjan Huisman, “Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning–based Prostate Cancer Detection Using Biparametric MRI”, Radiology: Artificial IntelligenceVol. 5, No. 5 https://doi.org/10.1148/ryai.230031

 

Pachetti, E.; Colantonio, S. 3D-Vision-Transformer Stacking Ensemble for Assessing Prostate Cancer Aggressiveness from T2w Images. Bioengineering 2023, 10, 1015. https://doi.org/10.3390/bioengineering10091015

 

Aikaterini Dovrou, Katerina Nikiforaki, Dimitris Zaridis, Georgios C. Manikis, Eugenia Mylona, Nikolaos Tachos, Manolis Tsiknakis, Dimitrios I. Fotiadis, Kostas Marias ,2023.“A segmentation-based method improving the performance of N4 bias field correction on T2weighted MR imaging data of the prostate”. Magnetic Resonance Imaging 101, 1-12 (2023),10.1016/j.mri.2023.03.012

 

Dimitrios I. Zaridis, Eugenia Mylona, Nikolaos Tachos, Vasileios C. Pezoulas, Grigorios Grigoriadis, Nikos Tsiknakis, Kostas Marias, Manolis Tsiknakis & Dimitrios I. Fotiadis ,2023.“Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones”. Scientific Reports 13 (1), 714 (2023), https://doi.org/10.1038/s41598-023-27671-8

Andrea Berti, Rossana Buongiorno, Gianluca Carloni, Claudia Caudai, Giulio Del Corso, Danila Germanese, Eva Pachetti, Maria Antonietta Pascali and Sara Colantonio, 2023.” Exploring the potentials and challenges of Artificial Intelligence in supporting clinical diagnostics and remote assistance for the health and well-being of individuals” ,Ital-IA 2023: 3rd National Conference on Artificial Intelligence, organized by CINI, May 29–31, 2023 

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-Bonmati, Leonor Cerdá Alberich, Karine Seymour, Samuel Boucher, Esther Ciarrocchi, Lauren Fromont, Jordi Rambla, Alexander Harms, Andrea Gutierrez, Martijn P. A. Starmans, Fred Prior, Josep Ll. Gelpi & Karim Lekadir ,2023.“Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects”. Eur Radiol Exp 7, 20 (2023), https://doi.org/10.1186/s41747-023-00336-x

Fanni Salvatore C., Febi Maria, Colligiani Leonardo, Volpi Federica, Ambrosini Ilaria, Tumminello Lorenzo, Aghakhanyan Gayane, Aringhieri Giacomo, Cioni Dania, Neri Emanuele, 2023.“A first look into radiomics application in testicular imaging: A systematic review”. Front. Radiol., 17 April 2023.10.3389/fradi.2023.1141499

Ana Rodrigues, Nuno Rodrigues, João Santinha, Maria V. Lisitskaya, Aycan Uysal, Celso Matos, Inês Domingues & Nickolas Papanikolaou,2023.“Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness”. Sci Rep 13, 6206 (2023). https://doi.org/10.1038/s41598-023-33339-0

Riccardo Albertoni, Sara Colantonio, Piotr Skrzypczyński, Jerzy Stefanowski, 2023. “Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues“,arXiv [cs.AI], https://doi.org/10.48550/arXiv.2302.12691

Arianna Defeudis, Jovana Panic, Giulia Nicoletti, Simone Mazzetti, Valentina Giannini and Daniele Regge, 2023. “Virtual biopsy in abdominal pathology: where do we stand?” BJR, Volume 4, Issue 1. https://doi.org/10.1259/bjro.20220055
Rodrigues, Nuno M., Sara Silva, Leonardo Vanneschi, and Nickolas Papanikolaou. 2023. “A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI” Cancers 15, no. 5: 1467. https://doi.org/10.3390/cancers15051467

2022

D. Zaridis, E. Mylona, N. Tachos, K. Marias, M. Tsiknakis and D. I. Fotiadis, “Fine-tuned feature selection to improve prostate segmentation via a fully connected meta-learner architecture,” 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece, 2022, pp. 01-04, doi: 10.1109/BHI56158.2022.9926929.

E. Mylona, D. Zaridis, N. Tachos, K. Marias, M. Tsiknakis and D. I. Fotiadis, “PROper-Net: A Deep-Learning Approach for Prostate’s Peripheral Zone Segmentation based on MR imaging,” 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), Palermo, Italy, 2022, pp. 1124-1128, doi: 10.1109/MELECON53508.2022.9843082.

Avtantil Dimitriadis, Eleftherios Trivizakis, Nikolaos Papanikolaou, Manolis Tsiknakis & Kostas Marias (2022), “Enhancing cancer differentiation with synthetic MRI examinations via generative models: a systematic review”, Insights into Imaging 13(188), doi:10.1186/s13244-022-01315-3

Haridimos Kondylakis, , Esther Ciarrocchi, Leonor Cerda-Alberich, , Ioanna Chouvarda, , Lauren A. Fromont, Jose Manuel Garcia-Aznar, Varvara Kalokyri, Alexandra Kosvyra, Dawn Walker, Guang Yang, Emanuele Neri (2022), “Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks”, European Radiology Experimental 2022 Jul 1;6(1):29. doi: 10.1186/s41747-022-00281-1.

Eva Pachetti, Sara Colantonio and Maria Antonietta Pascali (2022), “On the Effectiveness of 3D Vision Transformers for the Prediction of Prostate Cancer Aggressiveness” , MEDTX – International Conference on Image Analysis and Processing ICIAP21 23-26/05/2022. doi: https://doi.org/10.1007/978-3-031-13324-4_27

João Santinha, Linda Bianchini, Mário Figueiredo, Celso Matos, Alessandro Lascialfari, Nikolaos Papanikolaou, Marta Cremonesi, Barbara A. Jereczek-Fossa, Francesca Botta and Daniela Origgi (2022), “Discrimination of Tumor Texture Based on MRI Radiomic Features: Is There a Volume Threshold? A Phantom Study”, https://doi.org/10.3390/app12115465, on line 27/05/2022 by MDPI in Applied Sciences

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 (2022), “Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper”, https://doi.org/10.1186/s13244-022-01220-9, Online 10/05/2022

Giovanni Maimone, Giulia Nicoletti, Simone Mazzetti, Daniele Regge, Valentina Giannini (2022), “Comparison of Machine and Deep Learning models for automatic segmentation of prostate cancers on multiparametric MRI”, ΙΕΕΕMeMea 2022, 22-24/06/22,  Taormina

Haridimos Kondylakis, Stelios Sfakianakis, Varvara Kalokyri, Nikolaos Tachos, Dimitrios Fotiadis, Kostas Marias, Manolis Tsiknakis (2022), “Data Ingestion for AI in Prostate Cancer”, MIE2022, 27-30/5/22, Nice, DOI: 10.3233/SHTI220446, ON LINE: 25/05/2022

Haridimos Kondylakis, Stelios Sfakianakis, Varvara Kalokyri, Alexandros Kanterakis, Lefteris Koumakis, Eugenia Mylona, , Nikolaos Tachos, Dimitrios Fotiadis, Kostas Marias, Manolis Tsiknakis (2022), “AI Passport – Traceability for Trustworthy AI ” EMBC 2022 11 – 15/7/22, Glasgow.

Saha, Anindo; Twilt, Jasper Jonathan; Bosma, Joeran Sander; van Ginneken, Bram; Yakar, Derya; Elschot, Mattijs; Veltman, Jeroen; Fütterer, Jurgen; de Rooij, Maarten; Huisman, Henkjan (2022) “Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)“, doi: 10.5281/zenodo.6522364 , On line 05/05/2022

Zaridis Dimitris; Mylona Eugenia; Tachos Nikolaos; Marias Kostas; Tsiknakis Manolis; Fotiadis Dimitrios (2022), “A smart cropping pipeline to improve prostate’s peripheral zone segmentation on MRI using Deep Learning” – EAI Endorsed Transactions on Bioengineering and Bioinformatics, https://eudl.eu/doi/10.4108/eai.24-2-2022.173546. Οnline 22/02/2022

Elena Bertelli, Laura Mercatelli, Chiara Marzi, Eva Pachetti , Michela Baccini, Andrea Barucci, Sara Colantonio, Luca Gherardini, Lorenzo Lattavo, Maria Antonietta Pascali, , Simone Agostini, and Vittorio Miele (2022), “Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI” – frontiersin https://doi.org/10.3389/fonc.2021.802964. Οnline 17/01/2022

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 (2022), “Bridging gaps between images and data: a systematic update on imaging biobanks” – European Radiology https://doi.org/10.1007/s00330-021-08431-6 . Οnline 10/01/2022

2021

Karim Lekadir, Richard Osuala, Catherine Gallin, Noussair Lazrak, Kaisar Kushibar, Gianna Tsakou, Susanna Aussó, Leonor Cerdá Alberich, Kostas Marias, Manolis Tsiknakis, Sara Colantonio, Nickolas Papanikolaou, Zohaib Salahuddin, Henry C Woodruff, Philippe Lambin, Luis Martí-Bonmatí (2021). FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging. arXiv preprint arXiv:2109.09658.

Ana Rodrigues, João Santinha, Bernardo Galvão ,Celso Matos. Francisco M. Couto and Nickolas Papanikolaou (2021), “Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics” – Special Issue Radiomics/Radiogenomics in Cancer mdpi.com. Online 01/12/2021, https://doi.org/10.3390/cancers13236065

Eugenia Mylona, Dimitris Zaridis, Nikolaos Tachos, Dimitrios Fotiadis, Kostas Marias and Manolis Tsiknakis (2021), “A Deep Learning-based Cropping Technique to Improve Segmentation of Prostate’s Peripheral Zone” – 21st IEEE International Conference on BioInformatics and BioEngineering October 25-27, 2021, Kragujevac, Serbia *BEST STUDENT AWARD*

Daniela Condesso, Henrique Rodrigues, João Abrantes, João C. Costa (2021), RP Case Report nº 22: What is your diagnosis? “Case Report Quiz – Use of an MRI guided in-bore biopsy system for higher rates of cancer detection with real-time feedback with needle placement in the MRI system.”, Vol. 33 No. 1 (2021): Acta Radiológica Portuguesa, https://doi.org/10.25748/arp.24450

Daniela Condesso, Henrique Rodrigues, João Abrantes, João C. Costa (2021), ARP Case Report nº 22: Apical Anterior Prostate Lesion “Case Report Description – Use of an MRI guided in-bore biopsy system for higher rates of cancer detection with real-time feedback with needle placement in the MRI system“, Vol. 33 No. 2 (2021): Acta Radiológica Portuguesa, https://doi.org/10.25748/arp.25402

Anindo Saha, Joeran Bosma, Jasper Linmans, Matin Hosseinzadeh, Henkjan Huisman (2021), “Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI —Should Different Clinical Objectives Mandate Different Loss Functions?“, Medical Imaging Meets NeurIPS Workshop at 35th Conference on Neural Information Processing Systems (NeurIPS).

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 (2021), “A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation“, Frontiers in Oncology, Online 01/10/2021. https://doi.org/10.3389/fonc.2021.718155

Scapicchio, C., Gabelloni, M., Barucci, A. et al (2021), “A deep look into radiomics“. La radiologia medica – Official Journal of the Italian Society of Medical and Interventional Radiology, Online 02/07/2021. https://doi.org/10.1007/s11547-021-01389-x

A. Saha, M. Hosseinzadeh, H. Huisman (2021), “End-to-End Prostate Cancer Detection in bpMRI via 3D CNNs: Effect of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction“, MedIA: Medical Image Analysis, Online 29/06/2021. https://doi.org/10.1016/j.media.2021.102155

Giannini, V., Mazzetti, S., Cappello, G., Doronzio, V. M., Vassallo, L., Russo, F., Giacobbe, A., Muto, G., & Regge, D. (2021). Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers. Diagnostics (Basel, Switzerland), 11(6), 973. https://doi.org/10.3390/diagnostics11060973

A. Saha, M. Hosseinzadeh, H. Huisman (2020), “Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI“, Medical Imaging Meets NeurIPS Workshop – 34th Conference on Neural Information Processing Systems (NeurIPS), Vancouever, Canada.

Dimitrios G. Zaridis, Eugenia Mylona, Nikolaos S. Tachos, Kostas Marias, Nikolaos Papanikolaou, Manolis Tsiknakis, Dimitrios I. Fotiadis (2021), “A new smart-cropping pipeline for prostate segmentation using deep learning networks“, arxiv.org 2021. Online  07/07/2021. https://doi.org/10.48550/arXiv.2107.02476

Jasper J.Twilt, Kicky G. van Leeuwen, Henkjan J. Huisman, Jurgen J. Fütterer and Maarten de Rooij (2021), “Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review“, Diagnostics 2021. Online  26 /05/2021. https://doi.org/10.3390/diagnostics11060959

Highlights

AI in PCa imaging : The current status and future perspectives

AI in PCa imaging : The current status and future perspectives

Ιn recent years, magnetic resonance imaging (MRI) has transformed the prostate cancer (PCa) diagnostic pathway, based on the evidence of multiple high-level evidence studies (refs 4M, MRI first, and PROMIS). Taken together, the evidence indicates that prostate MRI...

Open Call To Include New Beneficiaries Into The EUCAIM Consortium

Open Call To Include New Beneficiaries Into The EUCAIM Consortium

The EUCAIM project is launching an open call for new beneficiaries to join the consortium. The objective of the EUCAIM open call is to: i) onboard new cancer image data holders, increasing the geographic dimensions, data modalities or cancer targets, and  ii) include...

Third Dissemination Event of the ProCAncer-I Project in Athens

Third Dissemination Event of the ProCAncer-I Project in Athens

ProCAncer-I will be organising the 3rd Dissemination Event at the 21st IEEE International Symposium on Biomedical Imaging, which will be held in Athens, Greece, May 27-30, 2024. ProCAncer-I will organise the Workshop “Integrating imaging Data and AI models for...

ProCAncer-I at the ECR 2024 in Vienna

ProCAncer-I at the ECR 2024 in Vienna

A great congress once more! Artificial Intelligence (AI) took the spotlight at the European Radiology Congress (ECR) held at the Austria Center Vienna from February 28 to March 3. Under the theme of 'Next Generation Radiology,' cutting-edge technologies were...

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