Publications

2024

  1. Artificial Intelligence-Enhanced Breast MRI
    Investigative Radiology
    R. Lo Gullo, E. Marcus, J. Huayanay, S. Eskreis-Winkler, S. Thakur, J. Teuwen, K. Pinker, 2024
  2. Artificial intelligence and explanation: How, why, and when to explain black boxes
    European Journal of Radiology
    E. Marcus, J. Teuwen, 2024
  3. Nodule detection and generation on chest X-rays: NODE21 Challenge
    IEEE Transactions on Medical Imaging
    E. Sogancioglu, B. Van Ginneken, F. Behrendt, M. Bengs, A. Schlaefer, M. Radu, D. Xu, K. Sheng, F. Scalzo, E. Marcus, S. Papa, J. Teuwen, E. T. Scholten, S. Schalekamp, N. Hendrix, C. Jacobs, W. Hendrix, C. I. Sánchez, K. Murphy, 2024
  4. AI Applications to Breast MRI: Today and Tomorrow.
    Journal of magnetic resonance imaging: JMRI
    R. Lo Gullo, J. Brunekreef, E. Marcus, L. K. Han, S. Eskreis-Winkler, S. B. Thakur, R. Mann, K. Groot Lipman, J. Teuwen, K. Pinker, 2024
  5. On retrospective k-space subsampling schemes for deep MRI reconstruction
    Magnetic Resonance Imaging
    G. Yiasemis, C. I. Sánchez, J. Sonke, J. Teuwen, 2024
  6. Image‐based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno‐oncology Biomarker Working Group on Breast Cancer
    The Journal of Pathology
    C. A. Jahangir, D. B. Page, G. Broeckx, C. A. Gonzalez, C. Burke, C. Murphy, J. S. Reis‐Filho, A. Ly, P. W. Harms, R. R. Gupta, M. Vieth, A. I. Hida, M. Kahila, Z. Kos, P. J. Van Diest, S. Verbandt, J. Thagaard, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, S. Adams, J. S. Almeida, I. Alvarado‐Cabrero, F. Azmoudeh‐Ardalan, S. Badve, N. B. Baharun, E. R. Bellolio, V. Bheemaraju, K. R. Blenman, L. Botinelly Mendonça Fujimoto, O. Burgues, A. Chardas, M. C. U. Cheang, F. Ciompi, L. A. Cooper, A. Coosemans, G. Corredor, F. L. Dantas Portela, F. Deman, S. Demaria, S. N. Dudgeon, M. Elghazawy, C. Fernandez‐Martín, S. Fineberg, S. B. Fox, J. M. Giltnane, S. Gnjatic, P. I. Gonzalez‐Ericsson, A. Grigoriadis, N. Halama, M. G. Hanna, A. Harbhajanka, S. N. Hart, J. Hartman, S. Hewitt, H. M. Horlings, Z. Husain, S. Irshad, E. A. Janssen, T. R. Kataoka, K. Kawaguchi, A. I. Khramtsov, U. Kiraz, P. Kirtani, L. L. Kodach, K. Korski, G. Akturk, E. Scott, A. Kovács, A. Lænkholm, C. Lang‐Schwarz, D. Larsimont, J. K. Lennerz, M. Lerousseau, X. Li, A. Madabhushi, S. K. Maley, V. Manur Narasimhamurthy, D. K. Marks, E. S. Mcdonald, R. Mehrotra, S. Michiels, D. Kharidehal, F. U. A. A. Minhas, S. Mittal, D. A. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault‐Llorca, R. D. Perera, C. J. Pinard, J. C. Pinto‐Cardenas, G. Pruneri, L. Pusztai, N. M. Rajpoot, B. L. Rapoport, T. T. Rau, J. M. Ribeiro, D. Rimm, A. Vincent‐Salomon, J. Saltz, S. Sayed, E. Hytopoulos, S. Mahon, K. P. Siziopikou, C. Sotiriou, A. Stenzinger, M. A. Sughayer, D. Sur, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. A. Thompson, T. Tramm, W. T. Tran, J. Van Der Laak, G. E. Verghese, G. Viale, N. Wahab, T. Walter, Y. Waumans, H. Y. Wen, W. Yang, Y. Yuan, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, E. Specht Stovgaard, R. Salgado, W. M. Gallagher, A. Rahman, 2024
  7. To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
    L. Han, T. Tan, T. Zhang, Y. Gao, X. Wang, V. Longo, S. Ventura-Díaz, A. D'Angelo, J. Teuwen, R. Mann, 2024
  8. Equivariant Multiscale Learned Invertible Reconstruction for Cone Beam CT
    N. Moriakov, J. Sonke, J. Teuwen, 2024
  9. Deep Cardiac MRI Reconstruction with ADMM
    Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers
    G. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2024
  10. IMPORTANT-Net: Integrated MRI multi-parametric increment fusion generator with attention network for synthesizing absent data
    Information Fusion
    T. Zhang, T. Tan, L. Han, X. Wang, Y. Gao, J. Van Dijk, A. Portaluri, A. Gonzalez-Huete, A. D’Angelo, C. Lu, J. Teuwen, R. Beets-Tan, Y. Sun, R. Mann, 2024, 108
  11. Optimizing CycleGAN design for CBCT-to-CT translation: insights into 2D vs 3D modeling, patch size, and the need for tailored evaluation metrics
    Medical Imaging 2024: Image Processing
    I. Hadzic, S. Pai, V. Trier Taasti, D. Bontempi, I. Zhovannik, R. Canters, J. J. Sonke, A. Dekker, J. Teuwen, A. Traverso, 2024
  12. The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
    J. Lyu, C. Qin, S. Wang, F. Wang, Y. Li, Z. Wang, K. Guo, C. Ouyang, M. Tänzer, M. Liu, L. Sun, M. Sun, Q. Li, Z. Shi, S. Hua, H. Li, Z. Chen, Z. Zhang, B. Xin, D. N. Metaxas, G. Yiasemis, J. Teuwen, L. Zhang, W. Chen, Y. Pang, X. Liu, A. Razumov, D. V. Dylov, Q. Dou, K. Yan, Y. Xue, Y. Du, J. Dietlmeier, C. Garcia-Cabrera, Z. A. Hemidi, N. Vogt, Z. Xu, Y. Zhang, Y. Chu, W. Chen, W. Bai, X. Zhuang, J. Qin, L. Wu, G. Yang, X. Qu, H. Wang, C. Wang, 2024
  13. Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.
    Cancer imaging: the official publication of the International Cancer Imaging Society
    S. Alaeikhanehshir, M. M. Voets, F. H. van Duijnhoven, E. H. Lips, E. J. Groen, M. C. J. van Oirsouw, S. E. Hwang, J. Y. Lo, J. Wesseling, R. M. Mann, J. Teuwen, 2024, 24;(1):48
  14. Abstract 4951: Integrative multi-omic machine learning model predicts neoadjuvant immunotherapy response using molecular data and deep learning-derived features from digital pathology
    Cancer Research
    L. V. Leek, V. Botha, V. A. Baxi, J. Teuwen, H. M. Horlings, S. D. Chasalow, J. V. D. Haar, L. F. Wessels, A. Debroy, E. E. Voest, 2024, 84;(6_Supplement):4951-4951
  15. End-to-end Adaptive Dynamic Subsampling and Reconstruction for Cardiac MRI
    G. Yiasemis, J. Sonke, J. Teuwen, 2024

2023

  1. Constrained Empirical Risk Minimization: Theory and Practice
    E. Marcus, R. Sheombarsing, J. Sonke, J. Teuwen, 2023
  2. STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data
    B. P. Y. Kwee, M. Messemaker, E. Marcus, G. Oliveira, W. Scheper, C. J. Wu, J. Teuwen, T. N. Schumacher, 2023
  3. Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
    J. Brunekreef, E. Marcus, R. Sheombarsing, J. Sonke, J. Teuwen, 2023
  4. Predicting up to 10 year breast cancer risk using longitudinal mammographic screening history
    X. Wang, T. Tan, Y. Gao, R. Su, T. Zhang, L. Han, J. Teuwen, A. D’Angelo, C. A. Drukker, M. K. Schmidt, R. Beets-Tan, N. Karssemeijer, R. Mann, 2023
  5. Visualize what you learn: a well-explainable joint-learning framework based on multi-view mammograms and associated reports
    Y. Gao, H. Zhou, X. Wang, T. Zhang, R. Tan, L. Han, L. Estacio, A. D’Angelo, J. Teuwen, R. Mann, T. Tan, 2023
  6. RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease
    Cell Reports Medicine
    T. Zhang, T. Tan, X. Wang, Y. Gao, L. Han, L. Balkenende, A. D’Angelo, L. Bao, H. M. Horlings, J. Teuwen, R. G. Beets-Tan, R. M. Mann, 2023
  7. Spatial analyses of immune cell infiltration in cancer: current methods and future directions. A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
    The Journal of Pathology
    D. B. Page, G. Broeckx, C. A. Jahangir, S. Verbandt, R. R. Gupta, J. Thagaard, R. Khiroya, Z. Kos, K. Abduljabbar, G. Acosta Haab, B. Acs, J. S. Almeida, I. Alvarado‐Cabrero, F. Azmoudeh‐Ardalan, S. Badve, N. B. Baharun, E. R. Bellolio, V. Bheemaraju, K. R. Blenman, L. Botinelly Mendonça Fujimoto, O. Burgues, M. C. U. Cheang, F. Ciompi, L. A. Cooper, A. Coosemans, G. Corredor, F. L. Dantas Portela, F. Deman, S. Demaria, S. N. Dudgeon, M. Elghazawy, S. Ely, C. Fernandez‐Martín, S. Fineberg, S. B. Fox, W. M. Gallagher, J. M. Giltnane, S. Gnjatic, P. I. Gonzalez‐Ericsson, A. Grigoriadis, N. Halama, M. G. Hanna, A. Harbhajanka, A. Hardas, S. N. Hart, J. Hartman, S. Hewitt, A. I. Hida, H. M. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. A. Janssen, M. Kahila, T. R. Kataoka, K. Kawaguchi, D. Kharidehal, A. I. Khramtsov, U. Kiraz, P. Kirtani, L. L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang‐Schwarz, D. Larsimont, J. K. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. K. Maley, V. Manur Narasimhamurthy, D. K. Marks, E. S. Mcdonald, R. Mehrotra, S. Michiels, F. U. A. A. Minhas, S. Mittal, D. A. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault‐Llorca, R. D. Perera, C. J. Pinard, J. C. Pinto‐Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. M. Rajpoot, B. L. Rapoport, T. T. Rau, J. S. Reis‐Filho, J. M. Ribeiro, D. Rimm, A. Salomon, M. Salto‐Tellez, J. Saltz, S. Sayed, K. P. Siziopikou, C. Sotiriou, A. Stenzinger, M. A. Sughayer, D. Sur, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. A. Thompson, T. Tramm, W. T. Tran, J. Van Der Laak, P. J. Van Diest, G. E. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Y. Wen, W. Yang, Y. Yuan, S. Adams, J. M. S. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado, E. Specht Stovgaard, G. Akturk, N. Bouchmaa, 2023
  8. Improving Lesion Volume Measurements on Digital Mammograms
    N. Moriakov, J. Peters, R. Mann, N. Karssemeijer, J. V. Dijck, M. Broeders, J. Teuwen, 2023
  9. Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working Group on Breast Cancer
    The Journal of Pathology
    J. Thagaard, G. Broeckx, D. B. Page, C. A. Jahangir, S. Verbandt, Z. Kos, R. Gupta, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, G. Akturk, J. S. Almeida, I. Alvarado‐Cabrero, M. Amgad, F. Azmoudeh‐Ardalan, S. Badve, N. B. Baharun, E. Balslev, E. R. Bellolio, V. Bheemaraju, K. R. Blenman, L. Botinelly Mendonça Fujimoto, N. Bouchmaa, O. Burgues, A. Chardas, M. Chon U Cheang, F. Ciompi, L. A. Cooper, A. Coosemans, G. Corredor, A. B. Dahl, F. L. Dantas Portela, F. Deman, S. Demaria, J. Doré Hansen, S. N. Dudgeon, T. Ebstrup, M. Elghazawy, C. Fernandez‐Martín, S. B. Fox, W. M. Gallagher, J. M. Giltnane, S. Gnjatic, P. I. Gonzalez‐Ericsson, A. Grigoriadis, N. Halama, M. G. Hanna, A. Harbhajanka, S. N. Hart, J. Hartman, S. Hauberg, S. Hewitt, A. I. Hida, H. M. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. A. Janssen, M. Kahila, T. R. Kataoka, K. Kawaguchi, D. Kharidehal, A. I. Khramtsov, U. Kiraz, P. Kirtani, L. L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang‐Schwarz, D. Larsimont, J. K. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. K. Maley, V. Manur Narasimhamurthy, D. K. Marks, E. S. Mcdonald, R. Mehrotra, S. Michiels, F. U. A. A. Minhas, S. Mittal, D. A. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault‐Llorca, R. D. Perera, C. J. Pinard, J. C. Pinto‐Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. M. Rajpoot, B. L. Rapoport, T. T. Rau, J. S. Reis‐Filho, J. M. Ribeiro, D. Rimm, A. Roslind, A. Vincent‐Salomon, M. Salto‐Tellez, J. Saltz, S. Sayed, E. Scott, K. P. Siziopikou, C. Sotiriou, A. Stenzinger, M. A. Sughayer, D. Sur, S. Fineberg, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. A. Thompson, T. Tramm, W. T. Tran, J. Van Der Laak, P. J. Van Diest, G. E. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Y. Wen, W. Yang, Y. Yuan, R. M. Zin, S. Adams, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado, E. Specht Stovgaard, 2023, 260;(5):498-513
  10. Development, validation, and simplification of a scanner‐specific CT simulator
    Medical Physics
    S. A. M. Tunissen, L. J. Oostveen, N. Moriakov, J. Teuwen, K. Michielsen, E. J. Smit, I. Sechopoulos, 2023
  11. vSHARP: variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse-Problems
    G. Yiasemis, N. Moriakov, J. Sonke, J. Teuwen, 2023
  12. Synthesis of Contrast-Enhanced Breast MRI Using T1- and Multi-b-Value DWI-Based Hierarchical Fusion Network with Attention Mechanism
    Lecture Notes in Computer Science
    T. Zhang, L. Han, A. D’Angelo, X. Wang, Y. Gao, C. Lu, J. Teuwen, R. Beets-Tan, T. Tan, R. Mann, 2023
  13. Beyond the AJR: A Breakthrough in the Use of Artificial Intelligence for Mammography in Screening for Breast Cancer
    American Journal of Roentgenology
    R. M. Mann, J. Teuwen, 2023
  14. End‐to‐end memory‐efficient reconstruction for cone beam CT
    Medical Physics
    N. Moriakov, J. Sonke, J. Teuwen, 2023, 12;(50):7579-7593
  15. JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction
    G. Yiasemis, N. Moriakov, C. I. Sánchez, J. Sonke, J. Teuwen, 2023
  16. Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI
    Medical Image Analysis
    L. Han, T. Tan, T. Zhang, Y. Huang, X. Wang, Y. Gao, J. Teuwen, R. Mann, 2023
  17. Clinicopathological and prognostic value of calcification morphology descriptors in ductal carcinoma in situ of the breast: a systematic review and meta-analysis
    Insights into Imaging
    M. M. Van Leeuwen, S. Doyle, A. W. Van Den Belt–Dusebout, S. Van Der Mierden, C. E. Loo, R. M. Mann, J. Teuwen, J. Wesseling, 2023, 14;(1)
  18. Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors
    Journal of Medical Imaging
    S. D. Verboom, M. Caballo, J. Peters, J. Gommers, D. Van Den Oever, M. J. M. Broeders, J. Teuwen, I. Sechopoulos, 2023, 11;(01)
  19. Nature of spatial universes in 3D Lorentzian quantum gravity
    Physical Review D
    J. Brunekreef, R. Loll, 2023, 107;(2)
  20. Joint machine learning and analytic track reconstruction for X-ray polarimetry with gas pixel detectors
    Astronomy & Astrophysics
    N. Cibrario, M. Negro, N. Moriakov, R. Bonino, L. Baldini, N. Di Lalla, L. Latronico, S. Maldera, A. Manfreda, N. Omodei, C. Sgró, S. Tugliani, 2023, 674
  21. IL-5-producing CD4+ T cells and eosinophils cooperate to enhance response to immune checkpoint blockade in breast cancer
    Cancer Cell
    O. S. Blomberg, L. Spagnuolo, H. Garner, L. Voorwerk, O. I. Isaeva, E. Van Dyk, N. Bakker, M. Chalabi, C. Klaver, M. Duijst, K. Kersten, M. Brüggemann, D. Pastoors, C. Hau, K. Vrijland, E. A. Raeven, D. Kaldenbach, K. Kos, I. S. Afonina, P. Kaptein, L. Hoes, W. S. Theelen, P. Baas, E. E. Voest, R. Beyaert, D. S. Thommen, L. F. Wessels, K. E. De Visser, M. Kok, 2023, 41;(1):106-123.e10
  22. Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping
    American Journal of Neuroradiology
    A. Zhylka, N. Sollmann, F. Kofler, A. Radwan, A. De Luca, J. Gempt, B. Wiestler, B. Menze, A. Schroeder, C. Zimmer, J. Kirschke, S. Sunaert, A. Leemans, S. Krieg, J. Pluim, 2023, 44;(3):283-290
  23. Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study
    Cell Reports Medicine
    J. M. Niehues, P. Quirke, N. P. West, H. I. Grabsch, M. Van Treeck, Y. Schirris, G. P. Veldhuizen, G. G. Hutchins, S. D. Richman, S. Foersch, T. J. Brinker, J. Fukuoka, A. Bychkov, W. Uegami, D. Truhn, H. Brenner, A. Brobeil, M. Hoffmeister, J. N. Kather, 2023, 4;(4):100980
  24. 1240P Multi-centric validation of an AI-based sTIL% scoring model for breast cancer H&E whole-slide images
    Annals of Oncology
    Y. Schirris, R. Voorthuis, M. Opdam, E. Gavves, R. De Menezes, S. Linn, H. Horlings, 2023, 34
  25. Predicting breast cancer types on and beyond molecular level in a multi-modal fashion
    npj Breast Cancer
    T. Zhang, T. Tan, L. Han, L. Appelman, J. Veltman, R. Wessels, K. M. Duvivier, C. Loo, Y. Gao, X. Wang, H. M. Horlings, R. G. H. Beets-Tan, R. M. Mann, 2023, 9;(1)
  26. Modeling Barrett’s Esophagus Progression Using Geometric Variational Autoencoders
    Cancer Prevention Through Early Detection
    V. Van Veldhuizen, S. Vadgama, O. De Boer, S. Meijer, E. J. Bekkers, 2023

2022

  1. FlowNet-PET: Unsupervised Learning to Perform Respiratory Motion Correction in PET Imaging
    T. O'Briain, C. Uribe, K. M. Yi, J. Teuwen, I. Sechopoulos, M. Bazalova-Carter, 2022
  2. Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry
    Clinical and Translational Radiation Oncology
    K. A. Wahid, S. Ahmed, R. He, L. V. Van Dijk, J. Teuwen, B. A. Mcdonald, V. Salama, A. S. Mohamed, T. Salzillo, C. Dede, N. Taku, S. Y. Lai, C. D. Fuller, M. A. Naser, 2022, 32
  3. DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer
    Medical Image Analysis
    Y. Schirris, E. Gavves, I. Nederlof, H. M. Horlings, J. Teuwen, 2022, 79
  4. Application of Deep Learning in Breast Cancer Imaging
    Seminars in Nuclear Medicine
    L. Balkenende, J. Teuwen, R. M. Mann, 2022, 52;(5):584-596
  5. Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations
    Frontiers in Neuroscience
    Y. Beauferris, J. Teuwen, D. Karkalousos, N. Moriakov, M. Caan, G. Yiasemis, L. Rodrigues, A. Lopes, H. Pedrini, L. Rittner, M. Dannecker, V. Studenyak, F. Gröger, D. Vyas, S. Faghih-Roohi, A. Kumar Jethi, J. Chandra Raju, M. Sivaprakasam, M. Lasby, N. Nogovitsyn, W. Loos, R. Frayne, R. Souza, 2022, 16
  6. DIRECT: Deep Image REConstruction Toolkit
    Journal of Open Source Software
    G. Yiasemis, N. Moriakov, D. Karkalousos, M. Caan, J. Teuwen, 2022, 7;(73):4278
  7. Deep MRI reconstruction with radial subsampling
    Medical Imaging 2022: Physics of Medical Imaging
    G. Yiasemis, C. Zhang, C. I. Sánchez, J. Sonke, J. Teuwen, 2022
  8. Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features
    Radiology
    R. Hou, L. J. Grimm, M. A. Mazurowski, J. R. Marks, L. M. King, C. C. Maley, T. Lynch, M. Van Oirsouw, K. Rogers, N. Stone, M. Wallis, J. Teuwen, J. Wesseling, E. S. Hwang, J. Y. Lo, 2022, 303;(1):54-62
  9. Exploiting the Dixon Method for a Robust Breast and Fibro-Glandular Tissue Segmentation in Breast MRI
    Diagnostics
    R. Samperna, N. Moriakov, N. Karssemeijer, J. Teuwen, R. M. Mann, 2022, 12;(7):1690