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1,571 results on '"Beets-Tan, Regina"'

1. Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms

Author

Wang, Xin, Tan, Tao, Gao, Yuan, Marcus, Eric, Han, Luyi, Portaluri, Antonio, Zhang, Tianyu, Lu, Chunyao, Liang, Xinglong, Beets-Tan, Regina, Teuwen, Jonas, and Mann, Ritse

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

Precision breast cancer (BC) risk assessment is crucial for developing individualized screening and prevention. Despite the promising potential of recent mammogram (MG) based deep learning models in predicting BC risk, they mostly overlook the 'time-to-future-event' ordering among patients and exhibit limited explorations into how they track history changes in breast tissue, thereby limiting their clinical application. In this work, we propose a novel method, named OA-BreaCR, to precisely model the ordinal relationship of the time to and between BC events while incorporating longitudinal breast tissue changes in a more explainable manner. We validate our method on public EMBED and inhouse datasets, comparing with existing BC risk prediction and time prediction methods. Our ordinal learning method OA-BreaCR outperforms existing methods in both BC risk and time-to-future-event prediction tasks. Additionally, ordinal heatmap visualizations show the model's attention over time. Our findings underscore the importance of interpretable and precise risk assessment for enhancing BC screening and prevention efforts. The code will be accessible to the public.

Published
2024

2. Towards Case-based Interpretability for Medical Federated Learning

Author

Latorre, Laura, Petrychenko, Liliana, Beets-Tan, Regina, Kopytova, Taisiya, and Silva, Wilson

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

We explore deep generative models to generate case-based explanations in a medical federated learning setting. Explaining AI model decisions through case-based interpretability is paramount to increasing trust and allowing widespread adoption of AI in clinical practice. However, medical AI training paradigms are shifting towards federated learning settings in order to comply with data protection regulations. In a federated scenario, past data is inaccessible to the current user. Thus, we use a deep generative model to generate synthetic examples that protect privacy and explain decisions. Our proof-of-concept focuses on pleural effusion diagnosis and uses publicly available Chest X-ray data., Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Published
2024

3. Enhancing Cross-Modal Medical Image Segmentation through Compositionality

Author

Eijpe, Aniek, Corbetta, Valentina, Chupetlovska, Kalina, Beets-Tan, Regina, and Silva, Wilson

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Cross-modal medical image segmentation presents a significant challenge, as different imaging modalities produce images with varying resolutions, contrasts, and appearances of anatomical structures. We introduce compositionality as an inductive bias in a cross-modal segmentation network to improve segmentation performance and interpretability while reducing complexity. The proposed network is an end-to-end cross-modal segmentation framework that enforces compositionality on the learned representations using learnable von Mises-Fisher kernels. These kernels facilitate content-style disentanglement in the learned representations, resulting in compositional content representations that are inherently interpretable and effectively disentangle different anatomical structures. The experimental results demonstrate enhanced segmentation performance and reduced computational costs on multiple medical datasets. Additionally, we demonstrate the interpretability of the learned compositional features. Code and checkpoints will be publicly available at: https://github.com/Trustworthy-AI-UU-NKI/Cross-Modal-Segmentation., Comment: 11 pages, 3 figures, 2 tables. Accepted at Deep Generative Models workshop @ MICCAI 2024 (DGM4MICCAI). This is the submitted manuscript with added link to github repo, funding acknowledgements and authors' names and affiliations. No further post submission improvements or corrections were integrated. Final version not published yet

Published
2024

4. FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation

Author

Schutte, Philip, Corbetta, Valentina, Beets-Tan, Regina, and Silva, Wilson

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

Federated Learning (FL) in Deep Learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. However, FL faces challenges with data heterogeneity among institutions, leading to suboptimal global models. Integrating Disentangled Representation Learning (DRL) in FL can enhance robustness by separating data into distinct representations. Existing DRL methods assume heterogeneity lies solely in style features, overlooking content-based variability like lesion size and shape. We propose FedGS, a novel FL aggregation method, to improve segmentation performance on small, under-represented targets while maintaining overall efficacy. FedGS demonstrates superior performance over FedAvg, particularly for small lesions, across PolypGen and LiTS datasets. The code and pre-trained checkpoints are available at the following link: https://github.com/Trustworthy-AI-UU-NKI/Federated-Learning-Disentanglement, Comment: 10 pages, 2 figures, 1 table, accepted at MICCAI 2024 Workshop on Distributed, Collaborative, & Federated Learning Workshop (DeCaF). This is the submitted manuscript with added link to github repo, funding acknowledgements and author names and affiliations. No further post submission improvements or corrections were integrated. Final version not published yet

Published
2024

7. Enhancing Cross-Modal Medical Image Segmentation Through Compositionality

Author

Eijpe, Aniek, Corbetta, Valentina, Chupetlovska, Kalina, Beets-Tan, Regina, Silva, Wilson, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mukhopadhyay, Anirban, editor, Oksuz, Ilkay, editor, Engelhardt, Sandy, editor, Mehrof, Dorit, editor, and Yuan, Yixuan, editor

Published
2025
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8. Imaging in pelvic exenteration—a multidisciplinary practice guide from the ESGAR-SAR-ESUR-PelvEx collaborative group

Author

Nougaret, Stephanie, Lambregts, Doenja M. J., Beets, Geerard L., Beets-Tan, Regina G. H., Blomqvist, Lennart, Burling, David, Denost, Quentin, Gambacorta, Maria A., Gui, Benedetta, Klopp, Ann, Lakhman, Yulia, Maturen, Kate E., Manfredi, Riccardo, Petkovska, Iva, Russo, Luca, Shinagare, Atul B., Stephenson, James A., Tolan, Damian, Venkatesan, Aradhana M., Quyn, Aaron J., and Forstner, Rosemarie

Published
2024
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9. Identifying the primary tumour in patients with cancer of unknown primary (CUP) using [18F]FDG PET/CT: a systematic review and individual patient data meta-analysis

Author

Willemse, Jeroen R. J., Lambregts, Doenja M. J., Balduzzi, Sara, Schats, Winnie, Snaebjornsson, Petur, Marchetti, Serena, Vollebergh, Marieke A., van Golen, Larissa W., Cheung, Zing, Vogel, Wouter V., Bodalal, Zuhir, Rostami, Sajjad, Gerke, Oke, Sivakumaran, Tharani, Beets-Tan, Regina G.H., and Lahaye, Max J.

Published
2024
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12. Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data

Author

Corbetta, Valentina, Beets-Tan, Regina, and Silva, Wilson

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

Multi-centre colonoscopy images from various medical centres exhibit distinct complicating factors and overlays that impact the image content, contingent on the specific acquisition centre. Existing Deep Segmentation networks struggle to achieve adequate generalizability in such data sets, and the currently available data augmentation methods do not effectively address these sources of data variability. As a solution, we introduce an innovative data augmentation approach centred on interpretability saliency maps, aimed at enhancing the generalizability of Deep Learning models within the realm of multi-centre colonoscopy image segmentation. The proposed augmentation technique demonstrates increased robustness across different segmentation models and domains. Thorough testing on a publicly available multi-centre dataset for polyp detection demonstrates the effectiveness and versatility of our approach, which is observed both in quantitative and qualitative results. The code is publicly available at: https://github.com/nki-radiology/interpretability_augmentation, Comment: 10 pages, 4 figures, 1 table, accepted at MICCAI 2023 Workshop on Machine Learning in Medical Imaging (MLMI)

Published
2023

13. DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning

Author

Wang, Xin, Tan, Tao, Gao, Yuan, Han, Luyi, Zhang, Tianyu, Lu, Chunyao, Beets-Tan, Regina, Su, Ruisheng, and Mann, Ritse

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public.

Published
2023

14. Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism

Author

Zhang, Tianyu, Han, Luyi, D'Angelo, Anna, Wang, Xin, Gao, Yuan, Lu, Chunyao, Teuwen, Jonas, Beets-Tan, Regina, Tan, Tao, and Mann, Ritse

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

Magnetic resonance imaging (MRI) is the most sensitive technique for breast cancer detection among current clinical imaging modalities. Contrast-enhanced MRI (CE-MRI) provides superior differentiation between tumors and invaded healthy tissue, and has become an indispensable technique in the detection and evaluation of cancer. However, the use of gadolinium-based contrast agents (GBCA) to obtain CE-MRI may be associated with nephrogenic systemic fibrosis and may lead to bioaccumulation in the brain, posing a potential risk to human health. Moreover, and likely more important, the use of gadolinium-based contrast agents requires the cannulation of a vein, and the injection of the contrast media which is cumbersome and places a burden on the patient. To reduce the use of contrast agents, diffusion-weighted imaging (DWI) is emerging as a key imaging technique, although currently usually complementing breast CE-MRI. In this study, we develop a multi-sequence fusion network to synthesize CE-MRI based on T1-weighted MRI and DWIs. DWIs with different b-values are fused to efficiently utilize the difference features of DWIs. Rather than proposing a pure data-driven approach, we invent a multi-sequence attention module to obtain refined feature maps, and leverage hierarchical representation information fused at different scales while utilizing the contributions from different sequences from a model-driven approach by introducing the weighted difference module. The results show that the multi-b-value DWI-based fusion model can potentially be used to synthesize CE-MRI, thus theoretically reducing or avoiding the use of GBCA, thereby minimizing the burden to patients. Our code is available at \url{https://github.com/Netherlands-Cancer-Institute/CE-MRI}., Comment: This paper has been accepted by MICCAI 2023

Published
2023

20. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Author

Kocak, Burak, Akinci D’Antonoli, Tugba, Mercaldo, Nathaniel, Alberich-Bayarri, Angel, Baessler, Bettina, Ambrosini, Ilaria, Andreychenko, Anna E., Bakas, Spyridon, Beets-Tan, Regina G. H., Bressem, Keno, Buvat, Irene, Cannella, Roberto, Cappellini, Luca Alessandro, Cavallo, Armando Ugo, Chepelev, Leonid L., Chu, Linda Chi Hang, Demircioglu, Aydin, deSouza, Nandita M., Dietzel, Matthias, Fanni, Salvatore Claudio, Fedorov, Andrey, Fournier, Laure S., Giannini, Valentina, Girometti, Rossano, Groot Lipman, Kevin B. W., Kalarakis, Georgios, Kelly, Brendan S., Klontzas, Michail E., Koh, Dow-Mu, Kotter, Elmar, Lee, Ho Yun, Maas, Mario, Marti-Bonmati, Luis, Müller, Henning, Obuchowski, Nancy, Orlhac, Fanny, Papanikolaou, Nikolaos, Petrash, Ekaterina, Pfaehler, Elisabeth, Pinto dos Santos, Daniel, Ponsiglione, Andrea, Sabater, Sebastià, Sardanelli, Francesco, Seeböck, Philipp, Sijtsema, Nanna M., Stanzione, Arnaldo, Traverso, Alberto, Ugga, Lorenzo, Vallières, Martin, van Dijk, Lisanne V., van Griethuysen, Joost J. M., van Hamersvelt, Robbert W., van Ooijen, Peter, Vernuccio, Federica, Wang, Alan, Williams, Stuart, Witowski, Jan, Zhang, Zhongyi, Zwanenburg, Alex, and Cuocolo, Renato

Published
2024
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21. Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study

Author

Cicalini, Ilaria, Chiarelli, Antonio Maria, Chiacchiaretta, Piero, Perpetuini, David, Rosa, Consuelo, Mastrodicasa, Domenico, d’Annibale, Martina, Trebeschi, Stefano, Serafini, Francesco Lorenzo, Cocco, Giulio, Narciso, Marco, Corvino, Antonio, Cinalli, Sebastiano, Genovesi, Domenico, Lanuti, Paola, Valentinuzzi, Silvia, Pieragostino, Damiana, Brocco, Davide, Beets-Tan, Regina G. H., Tinari, Nicola, Sensi, Stefano L., Stuppia, Liborio, Del Boccio, Piero, Caulo, Massimo, and Delli Pizzi, Andrea

Published
2024
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24. IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data

Author

Zhang, Tianyu, Tan, Tao, Han, Luyi, Wang, Xin, Gao, Yuan, Teuwen, Jonas, Beets-Tan, Regina, and Mann, Ritse

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

Magnetic resonance imaging (MRI) is highly sensitive for lesion detection in the breasts. Sequences obtained with different settings can capture the specific characteristics of lesions. Such multi-parameter MRI information has been shown to improve radiologist performance in lesion classification, as well as improving the performance of artificial intelligence models in various tasks. However, obtaining multi-parameter MRI makes the examination costly in both financial and time perspectives, and there may be safety concerns for special populations, thus making acquisition of the full spectrum of MRI sequences less durable. In this study, different than naive input fusion or feature concatenation from existing MRI parameters, a novel $\textbf{I}$ntegrated MRI $\textbf{M}$ulti-$\textbf{P}$arameter reinf$\textbf{O}$rcement fusion generato$\textbf{R}$ wi$\textbf{T}$h $\textbf{A}$tte$\textbf{NT}$ion Network (IMPORTANT-Net) is developed to generate missing parameters. First, the parameter reconstruction module is used to encode and restore the existing MRI parameters to obtain the corresponding latent representation information at any scale level. Then the multi-parameter fusion with attention module enables the interaction of the encoded information from different parameters through a set of algorithmic strategies, and applies different weights to the information through the attention mechanism after information fusion to obtain refined representation information. Finally, a reinforcement fusion scheme embedded in a $V^{-}$-shape generation module is used to combine the hierarchical representations to generate the missing MRI parameter. Results showed that our IMPORTANT-Net is capable of generating missing MRI parameters and outperforms comparable state-of-the-art networks. Our code is available at https://github.com/Netherlands-Cancer-Institute/MRI_IMPORTANT_NET.

Published
2023

25. Improving Neoadjuvant Therapy Response Prediction by Integrating Longitudinal Mammogram Generation with Cross-Modal Radiological Reports: A Vision-Language Alignment-Guided Model

Author

Gao, Yuan, Zhou, Hong-Yu, Wang, Xin, Zhang, Tianyu, Han, Luyi, Lu, Chunyao, Liang, Xinglong, Teuwen, Jonas, Beets-Tan, Regina, Tan, Tao, Mann, Ritse, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Linguraru, Marius George, editor, Dou, Qi, editor, Feragen, Aasa, editor, Giannarou, Stamatia, editor, Glocker, Ben, editor, Lekadir, Karim, editor, and Schnabel, Julia A., editor

Published
2024
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26. Interpretability-Guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data

Author

Corbetta, Valentina, Beets-Tan, Regina, Silva, Wilson, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cao, Xiaohuan, editor, Xu, Xuanang, editor, Rekik, Islem, editor, Cui, Zhiming, editor, and Ouyang, Xi, editor

Published
2024
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28. Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer

Author

Schurink, Niels W., van Kranen, Simon R., van Griethuysen, Joost J. M., Roberti, Sander, Snaebjornsson, Petur, Bakers, Frans C. H., de Bie, Shira H., Bosma, Gerlof P. T., Cappendijk, Vincent C., Geenen, Remy W. F., Neijenhuis, Peter A., Peterson, Gerald M., Veeken, Cornelis J., Vliegen, Roy F. A., Peters, Femke P., Bogveradze, Nino, el Khababi, Najim, Lahaye, Max J., Maas, Monique, Beets, Geerard L., Beets-Tan, Regina G. H., and Lambregts, Doenja M. J.

Published
2023
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29. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon

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

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

Published
2022
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35. Prognostic significance of MRI-detected extramural venous invasion according to grade and response to neo-adjuvant treatment in locally advanced rectal cancer A national cohort study after radiologic training and reassessment

Author

Aalbers, Arend G.J., van Aalten, Susanna M., Amelung, Femke J., Ankersmit, Marjolein, Antonisse, Imogeen E., Ashruf, Jesse F., Aukema, Tjeerd S., Avenarius, Henk, Bahadoer, Renu R., Bakers, Frans C.H., Bakker, Ilsalien S., Bangert, Fleur, Barendse, Renée M., Beekhuis, Heleen M.D., Beets, Geerard L., Bemelman, Willem A., Berbée, Maaike, de Bie, Shira H., Bisschops, Robert H.C., Blok, Robin D., van Bockel, Liselotte W., Boer, Anniek H., den Boer, Frank C., Boerma, Evert-Jan G., Boogerd, Leonora S.F., Borstlap, Jaap, Borstlap, Wernard A.A., Bouwman, Johanna E., Braak, Sicco J., Braat, Manon N.G.J.A., Bradshaw, Jennifer, Brandsma, Amarins T.A., van Breest Smallenburg, Vivian, van den Broek, Wim T., van der Burg, Sjirk W., Burger, Jacobus W.A., Burghgraef, Thijs A., ten Cate, David W.G., Ceha, Heleen M., Cnossen, Jeltsje S., Coebergh van den Braak, Robert R.J., Consten, Esther C.J., Corver, Maaike, Crolla, Rogier M.P.H., Curutchet, Sam, Daniëls-Gooszen, Alette W., Davids, Paul H.P., Dekker, Emmelie N., Dekker, Jan Willem T., Demirkiran, Ahmet, Derksen, Tyche, Diederik, Arjen L., Dinaux, Anne M., Dogan, Kemal, van Dop, Ilse M., Droogh-de Greve, Kitty E., Duijsens, Hanneke M.H., Dunker, Michalda S., Duyck, Johan, van Duyn, Eino B., van Egdom, Laurentine S.E., Eijlers, Bram, El-Massoudi, Youssef, van Elderen, Saskia, Emmen, Anouk M.L.H., Engelbrecht, Marc, van Erp, Anne C., van Essen, Jeroen A., Fabry, Hans F.J., Fassaert, Thomas, Feitsma, Eline A., Feshtali, Shirin S., Frietman, Bas, Furnée, Edgar J.B., van Geel, Anne M., Geijsen, Elisabeth D., van Geloven, Anna A.W., Gerhards, Michael F., Gielkens, Hugo, van Gils, Renza A.H., Goense, Lucas, Govaert, Marc J.P.M., van Grevenstein, Wilhelmina M.U., Joline de Groof, E., de Groot, Irene, de Haas, Robbert J., Hakkenbrak, Nadia A.G., den Hartogh, Mariska D., Heesink, Vera, Heikens, Joost T., Hendriksen, Ellen M., van den Hoek, Sjoerd, van der Hoeven, Erik J.R.J., Hoff, Christiaan, Hogewoning, Anna, Hogewoning, Cornelis R.C., Hoogendoorn, Stefan, van Hoorn, Francois, van der Hul, René L., van Hulst, Rieke, Imani, Farshad, Inberg, Bas, Intven, Martijn P.W., Janssen, Pedro, de Jong, Chris E.J., Jonkers, Jacoline, Jou-Valencia, Daniela, Keizers, Bas, Ketelaers, Stijn H.J., Knöps, Eva, van Koeverden, Sebastiaan, Kok, Sylvia, Kolderman, Stephanie E.M., de Korte, Fleur I., Kortekaas, Robert T.J., Korving, Julie C., Koster, Ingrid M., Krdzalic, Jasenko, Krielen, Pepijn, Kroese, Leonard F., Krul, Eveline J.T., Lahuis, Derk H.H., Lamme, Bas, van Landeghem, An A.G., Leijtens, Jeroen W.A., Leseman-Hoogenboom, Mathilde M., de Lijster, Manou S., Marsman, Martijn S., Martens, MilouH., Masselink, Ilse, van der Meij, Wout, Meijnen, Philip, Melenhorst, Jarno, de Mey, Dietrich J.L., Moelker-Galuzina, Julia, Morsink, Linda, Mulder, Erik J., Muller, Karin, Musters, Gijsbert D., Neijenhuis, Peter A., de Nes, Lindsey C.F., Nielen, M., van den Nieuwboer, Jan B.J., Nieuwenhuis, Jonanne F., Nonner, Joost, Noordman, Bo J., Nordkamp, Stefi, Olthof, Pim B., Oosterling, Steven J., Ootes, Daan, Oppedijk, Vera, Ott, Pieter, Paulusma, Ida, Peeters, Koen C.M.J., Pereboom, Ilona T.A., Peringa, Jan, Pironet, Zoë, Plate, Joost D.J., Polat, Fatih, Poodt, Ingrid G.M., Posma, Lisanne A.E., Prette, Jeroen F., Pultrum, Bareld B., Qaderi, Seyed M., van Rees, Jan M., Renger, Rutger-Jan, Rombouts, Anouk J.M., Roosen, Lodewijk J., Roskott-ten Brinke, Ellen A., Rothbarth, Joost, Rouw, Dennis B., Rozema, Tom, Rütten, Heidi, Rutten, Harm J.T., van der Sande, Marit E., Schaafsma, Boudewijn E., Schasfoort, Renske A., Scheurkogel, Merel M., Schouten van der Velden, Arjan P., Schreurs, Wilhelmina H., Schuivens, Puck M.E., Sietses, Colin, Simons, Petra C.G., Slob, Marjan J., Slooter, Gerrit D., van der Sluis, Martsje, Smalbroek, Bo P., Smits, Anke B., Spillenaar-Bilgen, Ernst J., Spruit, Patty H., Stam, Tanja C., Stoker, Jaap, Talsma, Aaldert K., Temmink, Sofieke J.D., The, G.Y. Mireille, Tielbeek, Jeroen A.W., van Tilborg, Aukje A.J.M., van Tilborg, Fiek, van Trier, Dorothée, Tuynman, Jurriaan B., van der Valk, Maxime J.M., Vanhooymissen, Inge J.S., Vasbinder, G. Boudewijn C., Veeken, Cornelis J., Velema, Laura A., van de Ven, Anthony W.H., Verdaasdonk, Emiel G.G., Verduin, Wouter M., Verhagen, Tim, Verheijen, Paul M., Vermaas, Maarten, Verrijssen, An-Sofie E., Verschuur, Anna V.D., Schaik, Harmke Verwoerd-van, Vliegen, Roy F.A., Voets, Sophie, Vogelaar, F. Jeroen, Vogelij, Clementine L.A., Vos-Westerman, Johanna, de Vries, Marianne, Vroemen, Joy C., van Vugt, Bas S.T., Wegdam, Johannes A., van Wely, Bob J., Westerterp, Marinke, van Westerveld, Paul P., van Westreenen, Henderik L., Wijma, Allard G., de Wilt, Johannes H.W., de Wit, Bart W.K., Wit, Fennie, Woensdregt, Karlijn, van Woerden, Victor, van der Wolf, Floor S.W., van der Wolk, Sander, Wybenga, Johannes M., van der Zaag, Edwin S., Zamaray, Bobby, Zandvoort, Herman J.A., van der Zee, Dennis, Zeilstra, Annette P., Zheng, Kang J., Zimmerman, David D.E., Zorgdrager, Marcel, Geffen, Eline G.M. van, Nederend, Joost, Sluckin, Tania C., Hazen, Sanne-Marije J.A., Horsthuis, Karin, Beets-Tan, Regina G.H., Marijnen, Corrie A.M., Tanis, Pieter J., and Kusters, Miranda

Published
2024
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37. Results of a diagnostic imaging audit in a randomised clinical trial in rectal cancer highlight the importance of careful planning and quality control

Author

Prata, Ilaria, Eriksson, Martina, Krdzalic, Jasenko, Kranenbarg, Elma Meershoek-Klein, Roodvoets, Annet G. H., Beets-Tan, Regina, van de Velde, Cornelis J. H., van Etten, Boudewijn, Hospers, Geke A. P., Glimelius, Bengt, Nilsson, Per J., Marijnen, Corrie A. M., Peeters, Koen C. M. J., and Blomqvist, Lennart K.

Published
2023
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42. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon

Published
2023
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44. Evaluation of National Surgical Practice for Lateral Lymph Nodes in Rectal Cancer in an Untrained Setting

Author

Sluckin, Tania C., Hazen, Sanne-Marije J. A., Horsthuis, Karin, Beets-Tan, Regina G. H., Aalbers, Arend G. J., Beets, Geerard L., Boerma, Evert-Jan G., Borstlap, Jaap, van Breest Smallenburg, Vivian, Burger, Jacobus W. A., Crolla, Rogier M. P. H., Daniëls-Gooszen, Alette W., Davids, Paul H. P., Dunker, Michalda S., Fabry, Hans F. J., Furnée, Edgar J. B., van Gils, Renza A. H., de Haas, Robbert J., Hoogendoorn, Stefan, van Koeverden, Sebastiaan, de Korte, Fleur I., Oosterling, Steven J., Peeters, Koen C. M. J., Posma, Lisanne A. E., Pultrum, Bareld B., Rothbarth, Joost, Rutten, Harm J. T., Schasfoort, Renske A., Schreurs, Wilhelmina H., Simons, Petra C. G., Smits, Anke B., Talsma, Aaldert K., The, G. Y. Mireille, van Tilborg, Fiek, Tuynman, Jurriaan B., Vanhooymissen, Inge J. S., van de Ven, Anthony W. H., Verdaasdonk, Emiel G. G., Vermaas, Maarten, Vliegen, Roy F. A., Vogelaar, F. Jeroen, de Vries, Marianne, Vroemen, Joy C., van Vugt, Sebastiaan T., Westerterp, Marinke, van Westreenen, Henderik L., de Wilt, Johannes H. W., van der Zaag, Edwin S., Zimmerman, David D. E., Marijnen, Corrie A. M., Tanis, Pieter J., and Kusters, Miranda

Published
2023
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45. Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI)

Author

Topff, Laurens, Groot Lipman, Kevin B. W., Guffens, Frederic, Wittenberg, Rianne, Bartels-Rutten, Annemarieke, van Veenendaal, Gerben, Hess, Mirco, Lamerigts, Kay, Wakkie, Joris, Ranschaert, Erik, Trebeschi, Stefano, Visser, Jacob J., and Beets-Tan, Regina G. H.

Published
2023
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50. Synthesis of Contrast-Enhanced Breast MRI Using T1- and Multi-b-Value DWI-Based Hierarchical Fusion Network with Attention Mechanism

Author

Zhang, Tianyu, Han, Luyi, D’Angelo, Anna, Wang, Xin, Gao, Yuan, Lu, Chunyao, Teuwen, Jonas, Beets-Tan, Regina, Tan, Tao, Mann, Ritse, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor

Published
2023
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