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Your search keyword '"Papanikolaou, Nickolas"' showing total 254 results
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254 results on '"Papanikolaou, Nickolas"'

1. Testing the Segment Anything Model on radiology data

Author

de Almeida, José Guilherme, Rodrigues, Nuno M., Silva, Sara, and Papanikolaou, Nickolas

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Deep learning models trained with large amounts of data have become a recent and effective approach to predictive problem solving -- these have become known as "foundation models" as they can be used as fundamental tools for other applications. While the paramount examples of image classification (earlier) and large language models (more recently) led the way, the Segment Anything Model (SAM) was recently proposed and stands as the first foundation model for image segmentation, trained on over 10 million images and with recourse to over 1 billion masks. However, the question remains -- what are the limits of this foundation? Given that magnetic resonance imaging (MRI) stands as an important method of diagnosis, we sought to understand whether SAM could be used for a few tasks of zero-shot segmentation using MRI data. Particularly, we wanted to know if selecting masks from the pool of SAM predictions could lead to good segmentations. Here, we provide a critical assessment of the performance of SAM on magnetic resonance imaging data. We show that, while acceptable in a very limited set of cases, the overall trend implies that these models are insufficient for MRI segmentation across the whole volume, but can provide good segmentations in a few, specific slices. More importantly, we note that while foundation models trained on natural images are set to become key aspects of predictive modelling, they may prove ineffective when used on other imaging modalities.

Published
2023

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

Author

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, and Martí-Bonmatí, Luis

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Addressing these concerns and risks, the FUTURE-AI framework has been proposed, which, sourced from a global multi-domain expert consensus, comprises guiding principles for increased trust, safety, and adoption for AI in healthcare. In this paper, we transform the general FUTURE-AI healthcare principles to a concise and specific AI implementation guide tailored to the needs of the medical imaging community. To this end, we carefully assess each building block of the FUTURE-AI framework consisting of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability, and respectively define concrete best practices based on accumulated AI implementation experiences from five large European projects on AI in Health Imaging. We accompany our concrete step-by-step medical imaging development guide with a practical AI solution maturity checklist, thus enabling AI development teams to design, evaluate, maintain, and deploy technically, clinically and ethically trustworthy imaging AI solutions into clinical practice., Comment: Please refer to arXiv:2309.12325 for the latest FUTURE-AI framework for healthcare

Published
2021

11. Corrigendum to “Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data” [Comput. Biol. Med. 17 (2024) 108216]

Author

Rodrigues, Nuno Miguel, primary, de Almeida, José Guilherme, additional, Castro Verde, Ana Sofia, additional, Gaivão, Ana Mascarenhas, additional, Bilreiro, Carlos, additional, Santiago, Inês, additional, Ip, Joana, additional, Belião, Sara, additional, Moreno, Raquel, additional, Matos, Celso, additional, Vanneschi, Leonardo, additional, Tsiknakis, Manolis, additional, Marias, Kostas, additional, Regge, Daniele, additional, Silva, Sara, additional, and Papanikolaou, Nickolas, additional

Published
2024
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13. Regularization Techniques in Radiomics: A Case Study on the Prediction of pCR in Breast Tumours and the Axilla

Author

Carrasquinha, Eunice, Santinha, João, Mongolin, Alexander, Lisitskiya, Maria, Ribeiro, Joana, Cardoso, Fátima, Matos, Celso, Vanneschi, Leonardo, Papanikolaou, Nickolas, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cazzaniga, Paolo, editor, Besozzi, Daniela, editor, Merelli, Ivan, editor, and Manzoni, Luca, editor

Published
2020
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14. Principles and Constraints of Nonrigid Registration

Author

Giavri, Zoi, Costa, Durval C., Papanikolaou, Nickolas, Kauczor, Hans-Ulrich, Series Editor, Parizel, Paul M., Series Editor, Peh, Wilfred C. G., Series Editor, Brady, Luther W., Honorary Editor, Lu, Jiade J., Series Editor, Beets-Tan, Regina G.H., editor, Oyen, Wim J. G., editor, and Valentini, Vincenzo, editor

Published
2020
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15. Breast cancer surgery with augmented reality

Author

Gouveia, Pedro F., Costa, Joana, Morgado, Pedro, Kates, Ronald, Pinto, David, Mavioso, Carlos, Anacleto, João, Martinho, Marta, Lopes, Daniel Simões, Ferreira, Arlindo R., Vavourakis, Vasileios, Hadjicharalambous, Myrianthi, Silva, Marco A., Papanikolaou, Nickolas, Alves, Celeste, Cardoso, Fatima, and Cardoso, Maria João

Published
2021
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32. Small Bowel

Author

Gourtsoyianni, Sofia, Gourtsoyiannis, Nicholas C., Papanikolaou, Nickolas, and Gourtsoyiannis, Nicholas C., editor

Published
2011
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37. 3D MRI Acquisition: Technique

Author

Papanikolaou, Nickolas, Karampekios, Spyros, Baert, A. L., editor, Knauth, M., editor, Sartor, K., editor, Neri, Emanuele, editor, Caramella, Davide, editor, and Bartolozzi, Carlo, editor

Published
2008
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44. A machine learning risk model based on preoperative computed tomography scan to predict postoperative outcomes after pancreatoduodenectomy

Author

Capretti, Giovanni, primary, Bonifacio, Cristiana, additional, De Palma, Crescenzo, additional, Nebbia, Martina, additional, Giannitto, Caterina, additional, Cancian, Pierandrea, additional, Laino, Maria Elena, additional, Balzarini, Luca, additional, Papanikolaou, Nickolas, additional, Savevski, Victor, additional, and Zerbi, Alessandro, additional

Published
2021
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45. Contributors

Author

Afnan, Jalil, primary, Alexander, Jeffrey A., additional, Alexander, Lauren F., additional, Bajpai, Surabhi, additional, Baker, Mark E., additional, Baker, Stephen R., additional, Balachandran, Aparna, additional, Balfe, Dennis M., additional, Balthazar, Emil J., additional, Barnard, Stuart A., additional, Ba-Ssalamah, Ahmed, additional, Bennett, Genevieve L., additional, Berggruen, Senta, additional, Berlin, Jonathan W., additional, Bissett, George S., additional, Bittane, Roi M., additional, Blake, Michael A., additional, Borghei, Peyman, additional, Boyd, Kevin P., additional, Brandwein, Warren M., additional, Bruining, David H., additional, Buck, James L., additional, Butler, Carina L., additional, Butros, Selim R., additional, Carucci, Laura R., additional, Chang, Wei-Chou, additional, Chinnappan, Raj R., additional, Choi, Byung Ihn, additional, Cooperberg, Peter L., additional, Dachman, Abraham H., additional, Ding, Alexander, additional, Donaldson, Carolyn K., additional, Eisenberg, Ronald L., additional, Erturk, Sukru Mehmet, additional, Farrell, Thomas A., additional, Feinstein, Kate A., additional, Fernbach, Sandra K., additional, Ferral, Hector, additional, Fintelmann, Florian J., additional, Fishman, Elliot K., additional, Fletcher, Joel G., additional, Fowler, Kathryn J., additional, Frazier, Aletta A., additional, Fulcher, Ann S., additional, Gabriel, Helena, additional, Gaca, Ana Maria, additional, Garcia-Reyes, Kirema, additional, Gayer, Gabriela, additional, Ghahremani, Gary G., additional, Glick, Seth N., additional, Gore, Margaret D., additional, Gore, Richard M., additional, Gourtsoyianni, Sofia, additional, Gourtsoyiannis, Nicholas C., additional, Green, Jared R., additional, Gualdi, Gianfranco, additional, Gupta, Rajan T., additional, Guttikonda, Ravi, additional, Halvorsen, Robert A., additional, Hammond, Nancy A., additional, Harisinghani, Mukesh G., additional, Hedgire, Sandeep S., additional, Hoff, Frederick L., additional, Hollingsworth, Caroline L., additional, Horton, Karen M., additional, Huang, Steven Y., additional, Huprich, James E., additional, Ivanovic, Aleksandar M., additional, Jacobs, Jill E., additional, Javors, Bruce R., additional, Jones, Bronwyn, additional, Kalra, Naveen, additional, Kambadakone, Avinash, additional, Kappil, Mariam M., additional, Keppke, Ana L., additional, Kim, David H., additional, Kim, Stanley Taeson, additional, Kitchin, Douglas R., additional, Kochman, Michael L., additional, Koh, Dow-Mu, additional, Krishna, J. Satheesh, additional, Kulkarni, Naveen, additional, Lappas, John C., additional, Laufer, Igor, additional, Lee, Jr, Fred T., additional, Lee, Jeong Min, additional, Levine, Marc S., additional, Levy, Angela D., additional, Lim-Dunham, Jennifer E., additional, Little, Mark D., additional, Low, Russell N., additional, Maglinte, Dean D.T., additional, Mahmutoglu, Abdullah, additional, Manning, Maria A., additional, Marn, Charles S., additional, Masselli, Gabriele, additional, McDermott, Shaunagh, additional, Megibow, Alec J., additional, Mehta, Uday K., additional, Mellnick, Vincent M., additional, Menias, Christine O., additional, Meranda, Joseph, additional, Messmer, James M., additional, Meyers, Arthur B., additional, Meyers, Morton A., additional, Miller, Frank H., additional, Morgan, Tara, additional, Mortele, Koenraad J., additional, Mueller, Peter R., additional, Mullan, Brian P., additional, Narra, Vamsi, additional, Nemcek, Jr, Albert A., additional, Newmark, Geraldine Mogavero, additional, Nicholas, Jennifer L., additional, Nikolaidis, Paul, additional, Ott, David J., additional, Owen, Joseph, additional, Ozkan, Orhan S., additional, Papanikolaou, Nickolas, additional, Patel, Mikin V., additional, Patel, Pritesh, additional, Paulson, Erik K., additional, Peterson, Christine M., additional, Pickhardt, Perry J., additional, Qayyum, Aliya, additional, Rabin, David N., additional, Raman, Siva P., additional, Rodgers, Peter M., additional, Ros, Pablo R., additional, Rubesin, Stephen E., additional, Sagebiel, Tara, additional, Sahani, Dushyant V., additional, Saini, Sanjay, additional, Saker, Martha Cotsen, additional, Salem, Riad, additional, Sandrasegaran, Kumar, additional, Sanyal, Rupan, additional, Scheirey, Christopher D., additional, Scholz, Francis J., additional, Seyal, Adeel R., additional, Shelly, Martin J., additional, Sherbahn, Linda C., additional, Shirkhoda, Ali, additional, Silva, Ana Catarina, additional, Silverman, Paul M., additional, Silverman, Stuart G., additional, Silvers, Robert I., additional, Singh, Ajay K., additional, Skucas, Jovitas, additional, Smith, Gail S., additional, Somers, Sat, additional, Stanson, Anthony W., additional, Summers, Allison L., additional, Szucs, Richard A., additional, Talamonti, Mark, additional, Taylor, Andrew J., additional, Thakrar, Darshit J., additional, Thakrar, Kiran H., additional, Thian, Yee Liang, additional, Thoeni, Ruedi F., additional, Thomas, Stephen, additional, Thompson, William Moreau, additional, Tirkes, Temel, additional, Turner, Mary Ann, additional, Uyeda, Jennifer W., additional, Vandermeer, Fauzia Q., additional, Vogelzang, Robert L., additional, Vos, Patrick M., additional, Wehrli, Natasha, additional, Wenzke, Daniel R., additional, Wolf, Ellen L., additional, Wong-You-Cheong, Jade J., additional, Wood, Cecil G., additional, Woods, Michael A., additional, Yaghmai, Vahid, additional, and Yeh, Benjamin M., additional

Published
2015
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47. A case study on the prediction of pCR in breast tumours and the axilla

Author

Carrasquinha, Eunice, Santinha, João, Mongolin, Alexander, Lisitskiya, Maria, Ribeiro, Joana, Cardoso, Fátima, Matos, Celso, Vanneschi, Leonardo, Papanikolaou, Nickolas, NOVA Information Management School (NOVA IMS), and Information Management Research Center (MagIC) - NOVA Information Management School

Subjects
High-dimensional data, Breast cancer, SDG 3 - Good Health and Well-being, Radiomic features, Regularization techniques, Theoretical Computer Science, Computer Science(all)
Abstract

Carrasquinha, E., Santinha, J., Mongolin, A., Lisitskiya, M., Ribeiro, J., Cardoso, F., Matos, C., Vanneschi, L., & Papanikolaou, N. (2020). Regularization techniques in radiomics: A case study on the prediction of pCR in breast tumours and the axilla. In P. Cazzaniga, D. Besozzi, I. Merelli, & L. Manzoni (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics: 16th International Meeting, CIBB 2019, Revised Selected Papers (pp. 271-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12313 LNBI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-63061-4_24 Clinicians have shown an increasing interest in quantitative imaging for precision medicine. Imaging features can extract distinct phenotypic differences of tumours, potentially they can be used as a non-invasive prognostic tool and contribute for a better prediction of pathological Complete Response (pCR). However, the high-dimensional nature of the data brings many constraints, for which several approaches have been considered, with regularization techniques in the cutting-edge research front. In this work, classic lasso, ridge and the recently proposed priority-lasso are applied to high-dimensional imaging data, regarding a binary outcome. A breast cancer dataset, with radiomics, clinical and pathological information as features, was used. The application of sparsity techniques to the dataset enabled the selection of relevant features extracted in MRI of breast cancer patients, in order to identify the accuracy of those features and predict the pCR in the breast and the axilla. authorsversion published

Published
2020

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