2022

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”,  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

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.

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 tool accurately predicts tumour regrowth in cancer patients https://www.theguardian.com/society/2022/apr/23/cancer-ai-tool-predicts-tumour-regrowth?CMP=share_btn_tw

Our statement "Considerations for artificial intelligence clinical impact in oncologic imaging: an AI4HI position paper" is out! @primage_project @EuCanImage @chaimeleon_eu @ProCAncer_I
https://insightsimaging.springeropen.com/articles/10.1186/s13244-022-01220-9

May is #National_Cancer_Research_Month! Thanks to the spectacular advances made by researchers, more people are living longer and with a good quality of life.
A big thank you to the people who make it happen!
#H2020 #healthIT #AI #Cancer #EU_HEALTH #NCRM22 #ResearchSavesLives

Hoy hemos realizado la 1a biopsia robótica de próstata dirigida en RM de la nueva @ClinicaGirona. Unico centro en #Espana en realizar este procedimiento preciso, rápido, no invasivo y seguro para detectar el #cáncerdepróstata, habiéndo realizado +100
@SERAM_RX @sediabdomen

Load More...

Stay in touch!