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Your search keyword '"Marias, Kostas"' showing total 14 results
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14 results on '"Marias, Kostas"'

1. MAMA-MIA: A Large-Scale Multi-Center Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations

Author

Garrucho, Lidia, Reidel, Claire-Anne, Kushibar, Kaisar, Joshi, Smriti, Osuala, Richard, Tsirikoglou, Apostolia, Bobowicz, Maciej, del Riego, Javier, Catanese, Alessandro, Gwoździewicz, Katarzyna, Cosaka, Maria-Laura, Abo-Elhoda, Pasant M., Tantawy, Sara W., Sakrana, Shorouq S., Shawky-Abdelfatah, Norhan O., Abdo-Salem, Amr Muhammad, Kozana, Androniki, Divjak, Eugen, Ivanac, Gordana, Nikiforaki, Katerina, Klontzas, Michail E., García-Dosdá, Rosa, Gulsun-Akpinar, Meltem, Lafcı, Oğuz, Mann, Ritse, Martín-Isla, Carlos, Prior, Fred, Marias, Kostas, Starmans, Martijn P. A., Strand, Fredrik, Díaz, Oliver, Igual, Laura, and Lekadir, Karim

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

Current research in breast cancer Magnetic Resonance Imaging (MRI), especially with Artificial Intelligence (AI), faces challenges due to the lack of expert segmentations. To address this, we introduce the MAMA-MIA dataset, comprising 1506 multi-center dynamic contrast-enhanced MRI cases with expert segmentations of primary tumors and non-mass enhancement areas. These cases were sourced from four publicly available collections in The Cancer Imaging Archive (TCIA). Initially, we trained a deep learning model to automatically segment the cases, generating preliminary segmentations that significantly reduced expert segmentation time. Sixteen experts, averaging 9 years of experience in breast cancer, then corrected these segmentations, resulting in the final expert segmentations. Additionally, two radiologists conducted a visual inspection of the automatic segmentations to support future quality control studies. Alongside the expert segmentations, we provide 49 harmonized demographic and clinical variables and the pretrained weights of the well-known nnUNet architecture trained using the DCE-MRI full-images and expert segmentations. This dataset aims to accelerate the development and benchmarking of deep learning models and foster innovation in breast cancer diagnostics and treatment planning., Comment: 15 paes, 7 figures, 3 tables

Published
2024

9. LKB1 Loss Correlates with STING Loss and, in Cooperation with β-Catenin Membranous Loss, Indicates Poor Prognosis in Patients with Operable Non-Small Cell Lung Cancer

Author

Lagoudaki, Eleni D., primary, Koutsopoulos, Anastasios V., additional, Sfakianaki, Maria, additional, Papadaki, Chara, additional, Manikis, Georgios C., additional, Voutsina, Alexandra, additional, Trypaki, Maria, additional, Tsakalaki, Eleftheria, additional, Fiolitaki, Georgia, additional, Hatzidaki, Dora, additional, Yiachnakis, Emmanuel, additional, Koumaki, Dimitra, additional, Mavroudis, Dimitrios, additional, Tzardi, Maria, additional, Stathopoulos, Efstathios N., additional, Marias, Kostas, additional, Georgoulias, Vassilis, additional, and Souglakos, John, additional

Published
2024
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12. 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. Unveiling the Power of Model-Agnostic Multiscale Analysis for Enhancing Artificial Intelligence Models in Breast Cancer Histopathology Images

Author

Tsiknakis, Nikos, Manikis, Georgios, Tzoras, Evangelos, Salgkamis, Dimitrios, Vidal, Joan Martinez, Wang, Kang, Zaridis, Dimitris, Sifakis, Emmanouil, Zerdes, Ioannis, Bergh, Jonas, Hartman, Johan, Acs, Balazs, Marias, Kostas, and Foukakis, Theodoros

Abstract

Developing AI models for digital pathology has traditionally relied on single-scale analysis of histopathology slides. However, a whole slide image is a rich digital representation of the tissue, captured at various magnification levels. Limiting our analysis to a single scale overlooks critical information, spanning from intricate high-resolution cellular details to broad low-resolution tissue structures. In this study, we propose a model-agnostic multiresolution feature aggregation framework tailored for the analysis of histopathology slides in the context of breast cancer, on a multicohort dataset of 2038 patient samples. We have adapted 9 state-of-the-art multiple instance learning models on our multi-scale methodology and evaluated their performance on grade prediction, TP53 mutation status prediction and survival prediction. The results prove the dominance of the multiresolution methodology, and specifically, concatenating or linearly transforming via a learnable layer the feature vectors of image patches from a high (20x) and low (10x) magnification factors achieve improved performance for all prediction tasks across domain-specific and imagenet-based features. On the contrary, the performance of uniresolution baseline models was not consistent across domain-specific and imagenet-based features. Moreover, we shed light on the inherent inconsistencies observed in models trained on whole-tissue-sections when validated against biopsy-based datasets. Despite these challenges, our findings underscore the superiority of multiresolution analysis over uniresolution methods. Finally, cross-scale analysis also benefits the explainability aspects of attention-based architectures, since one can extract attention maps at the tissue- and cell-levels, improving the interpretation of the model's decision.

Published
2024
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14. Documenting the de-identification process of clinical and imaging data for AI for health imaging projects.

Author

Kondylakis, Haridimos, Catalan, Rocio, Alabart, Sara Martinez, Barelle, Caroline, Bizopoulos, Paschalis, Bobowicz, Maciej, Bona, Jonathan, Fotiadis, Dimitrios I., Garcia, Teresa, Gomez, Ignacio, Jimenez-Pastor, Ana, Karatzanis, Giannis, Lekadir, Karim, Kogut-Czarkowska, Magdalena, Lalas, Antonios, Marias, Kostas, Marti-Bonmati, Luis, Munuera, Jose, Nikiforaki, Katerina, and Pelissier, Manon

Subjects
DIAGNOSTIC imaging, COMPUTER-assisted image analysis (Medicine), ARTIFICIAL intelligence
Abstract

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. Critical relevance statement: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. Key Points: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects. [ABSTRACT FROM AUTHOR]

Published
2024
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