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140 results on '"Bobowicz, Maciej"'

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

2. FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Author

Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, and Starmans, Martijn P A

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.0, I.4.0, I.5.0
Abstract

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

Published
2023

3. 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, Pelissier, Manon, Prior, Fred, Rutherford, Michael, Saint-Aubert, Laure, Sakellariou, Zisis, Seymour, Karine, Trouillard, Thomas, Votis, Konstantinos, and Tsiknakis, Manolis

Published
2024
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4. High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

Author

Garrucho, Lidia, Kushibar, Kaisar, Osuala, Richard, Diaz, Oliver, Catanese, Alessandro, del Riego, Javier, Bobowicz, Maciej, Strand, Fredrik, Igual, Laura, and Lekadir, Karim

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

Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in highdensity breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in highresolution mammograms. The training images were split by breast density BIRADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in models trained with small datasets and improved the domain generalization of the models trained with large databases. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist., Comment: 9 figures, 4 tables

Published
2022
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10. Author Correction: Risk factors for serious morbidity, prolonged length of stay and hospital readmission after laparoscopic appendectomy - results from Pol-LA (Polish Laparoscopic Appendectomy) multicenter large cohort study

Author

Walędziak, Maciej, Lasek, Anna, Wysocki, Michał, Su, Michael, Bobowicz, Maciej, Myśliwiec, Piotr, Astapczyk, Kamil, Burdzel, Mateusz, Chruściel, Karolina, Cygan, Rafał, Czubek, Wojciech, Dowgiałło-Wnukiewicz, Natalia, Droś, Jakub, Franczak, Paula, Hołówko, Wacław, Kacprzyk, Artur, Karcz, Wojciech Konrad, Kenig, Jakub, Konrad, Paweł, Kopiejć, Arkadiusz, Kot, Adam, Krakowska, Karolina, Kukla, Maciej, Leszko, Agnieszka, Łozowski, Leszek, Major, Piotr, Makarewicz, Wojciech, Malinowska-Torbicz, Paulina, Matyja, Maciej, Michalik, Maciej, Niekurzak, Adam, Nowiński, Damian, Ostaszewski, Radomir, Pabis, Małgorzata, Polańska-Płachta, Małgorzata, Rubinkiewicz, Mateusz, Stefura, Tomasz, Stępień, Anna, Szabat, Paweł, Śmiechowski, Rafał, Tomaszewski, Sebastian, von Ehrlich-Treuenstätt, Viktor, Wasilczuk, Maciej, Wierdak, Mateusz, Wojdyła, Anna, Wroński, Jan Wojciech, Zwolakiewicz, Leszek, and Pędziwiatr, Michał

Published
2019
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11. Risk factors for serious morbidity, prolonged length of stay and hospital readmission after laparoscopic appendectomy - results from Pol-LA (Polish Laparoscopic Appendectomy) multicenter large cohort study

Author

Walędziak, Maciej, Lasek, Anna, Wysocki, Michał, Su, Michael, Bobowicz, Maciej, Myśliwiec, Piotr, Astapczyk, Kamil, Burdzel, Mateusz, Chruściel, Karolina, Cygan, Rafał, Czubek, Wojciech, Dowgiałło-Wnukiewicz, Natalia, Droś, Jakub, Franczak, Paula, Hołówko, Wacław, Kacprzyk, Artur, Karcz, Wojciech Konrad, Kenig, Jakub, Konrad, Paweł, Kopiejć, Arkadiusz, Kot, Adam, Krakowska, Karolina, Kukla, Maciej, Leszko, Agnieszka, Łozowski, Leszek, Major, Piotr, Makarewicz, Wojciech, Malinowska-Torbicz, Paulina, Matyja, Maciej, Michalik, Maciej, Niekurzak, Adam, Nowiński, Damian, Ostaszewski, Radomir, Pabis, Małgorzata, Polańska-Płachta, Małgorzata, Rubinkiewicz, Mateusz, Stefura, Tomasz, Stępień, Anna, Szabat, Paweł, Śmiechowski, Rafał, Tomaszewski, Sebastian, von Ehrlich-Treuenstätt, Viktor, Wasilczuk, Maciej, Wierdak, Mateusz, Wojdyła, Anna, Wroński, Jan Wojciech, Zwolakiewicz, Leszek, and Pędziwiatr, Michał

Published
2019
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13. Biases in artificial intelligence algorithm creation for breast cancer detection and classification in full field digital mammography

Author

Bobowicz, Maciej

Subjects
CAD, Breast, Mammography, Cancer
Abstract

Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: Participants will learn how to identify different sources of bias during the AI tools construction with some basic rules for bias...

Published
2023
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14. The significant impact of age on the clinical outcomes of laparoscopic appendectomy: Results from the Polish Laparoscopic Appendectomy multicenter large cohort study

Author

Lasek, Anna, Pędziwiatr, Michał, Kenig, Jakub, Walędziak, Maciej, Wysocki, Michał, Mavrikis, Judene, Myśliwiec, Piotr, Bobowicz, Maciej, Astapczyk, Kamil, Burdzel, Mateusz, Chruściel, Karolina, Cygan, Rafał, Czubek, Wojciech, Dowgiałło-Wnukiewicz, Natalia, Droś, Jakub, Franczak, Paulina, Hołówko, Wacław, Kacprzyk, Artur, Karcz, Wojciech Konrad, Konrad, Paweł, Kopiejć, Arkadiusz, Kot, Adam, Krakowska, Karolina, Kukla, Maciej, Leszko, Agnieszka, Łozowski, Leszek, Major, Piotr, Makarewicz, Wojciech, Malinowska-Torbicz, Paulina, Matyja, Maciej, Michalik, Maciej, Niekurzak, Adam, Nowiński, Damian, Ostaszewski, Radomir, Pabis, Małgorzata, Polańska-Płachta, Małgorzata, Rubinkiewicz, Mateusz, Stefura, Tomasz, Stępień, Anna, Szabat, Paweł, Śmiechowski, Rafał, Tomaszewski, Sebastian, von Ehrlich-Treuenstätt, Victor, Wasilczuk, Maciej, Wojdyła, Anna, Wroński, Jan Wojciech, and Zwolakiewicz, Leszek

Published
2018
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22. Risk factors for intraabdominal abscess formation after laparoscopic appendectomy – results from the Pol-LA (Polish Laparoscopic Appendectomy) multicenter large cohort study

Author

Lasek, Anna, primary, Pędziwiatr, Michał, additional, Wysocki, Michał, additional, Mavrikis, Judene, additional, Myśliwiec, Piotr, additional, Stefura, Tomasz, additional, Bobowicz, Maciej, additional, Major, Piotr, additional, Rubinkiewicz, Mateusz, additional, and (Polish Laparoscopic Appendectomy) Collaborative Study Group, Pol-LA, additional

Published
2019
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26. Keratin 7 expression in lymph node metastases but not in the primary tumour correlates with distant metastases and poor prognosis in colon carcinoma

Author

Czapiewski, Piotr, primary, Bobowicz, Maciej, additional, Pęksa, Rafał, additional, Skrzypski, Marcin, additional, Gorczyński, Adam, additional, Szczepańska-Michalska, Kamila, additional, Korwat, Aleksandra, additional, Jankowski, Michał, additional, Zegarski, Wojciech, additional, Szulgo-Paczkowska, Anna, additional, Polec, Tomasz, additional, Piątek, Michał, additional, Skokowski, Jarosław, additional, Haybaeck, Johannes, additional, Żaczek, Anna, additional, and Biernat, Wojciech, additional

Published
2016
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