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Maier-Hein, Lena; Reinke, Annika; Godau, Patrick; Tizabi, Minu D; Buettner, Florian; Christodoulou, Evangelia; Glocker, Ben; Isensee, Fabian; Kleesiek, Jens; Kozubek, Michal; Reyes, Mauricio; Riegler, Michael A; Wiesenfarth, Manuel; Kavur, A Emre; Sudre, Carole H; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Rädsch, Tim; Acion, Laura; ... (2024). Metrics reloaded: recommendations for image analysis validation. Nature methods, 21(2), pp. 195-212. Nature Publishing Group 10.1038/s41592-023-02151-z
Reinke, Annika; Tizabi, Minu D; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Kavur, A Emre; Rädsch, Tim; Sudre, Carole H; Acion, Laura; Antonelli, Michela; Arbel, Tal; Bakas, Spyridon; Benis, Arriel; Buettner, Florian; Cardoso, M Jorge; Cheplygina, Veronika; Chen, Jianxu; Christodoulou, Evangelia; Cimini, Beth A; Farahani, Keyvan; ... (2024). Understanding metric-related pitfalls in image analysis validation. Nature methods, 21(2), pp. 182-194. Nature Publishing Group 10.1038/s41592-023-02150-0
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; ... (2023). Author Correction: Federated learning enables big data for rare cancer boundary detection. Nature communications, 14(1), p. 436. Nature Publishing Group 10.1038/s41467-023-36188-7
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; ... (2022). Federated learning enables big data for rare cancer boundary detection. Nature communications, 13(1), p. 7346. Nature Publishing Group 10.1038/s41467-022-33407-5
Mehta, Raghav; Filos, Angelos; Baid, Ujjwal; Sako, Chiharu; McKinley, Richard; Rebsamen, Michael; Dätwyler, Katrin; Meier, Raphael; Radojewski, Piotr; Murugesan, Gowtham Krishnan; Nalawade, Sahil; Ganesh, Chandan; Wagner, Ben; Yu, Fang F; Fei, Baowei; Madhuranthakam, Ananth J; Maldjian, Joseph A; Daza, Laura; Gómez, Catalina; Arbeláez, Pablo; ... (2022). QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results. The journal of machine learning for biomedical imaging, 2022 MELBA
Menze, Bjoern; Isensee, Fabian; Wiest, Roland; Wiestler, Bene; Maier-Hein, Klaus; Reyes, Mauricio; Bakas, Spyridon (2021). Analyzing Magnetic Resonance Imaging Data from Glioma Patients using Deep Learning. Computerized medical imaging and graphics, 88, p. 101828. Elsevier 10.1016/j.compmedimag.2020.101828
Guo, Rui; Hu, Xiaobin; Song, Haoming; Xu, Pengpeng; Xu, Haoping; Rominger, Axel; Lin, Xiaozhu; Menze, Bjoern; Li, Biao; Shi, Kuangyu (2021). Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type. European journal of nuclear medicine and molecular imaging, 48(10), pp. 3151-3161. Springer 10.1007/s00259-021-05232-3
Hu, Xiaobin; Guo, Rui; Chen, Jieneng; Li, Hongwei; Waldmannstetter, Diana; Zhao, Yu; Li, Biao; Shi, Kuangyu; Menze, Bjoern (2020). Coarse-to-Fine Adversarial Networks and Zone-Based Uncertainty Analysis for NK/T-Cell Lymphoma Segmentation in CT/PET Images. IEEE journal of biomedical and health informatics, 24(9), pp. 2599-2608. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2020.2972694
Zhao, Yu; Gafita, Andrei; Vollnberg, Bernd; Tetteh, Giles; Haupt, Fabian; Afshar-Oromieh, Ali; Menze, Bjoern; Eiber, Matthias; Rominger, Axel; Shi, Kuangyu (2020). Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT. European journal of nuclear medicine and molecular imaging, 47(3), pp. 603-613. Springer-Verlag 10.1007/s00259-019-04606-y
Zhao, Yu; Gafita, Andrei; Tetteh, Giles; Haupt, Fabian; Afshar Oromieh, Ali; Menze, Bjoern; Eiber, Matthias; Rominger, Axel; Shi, Kuangyu (2019). Deep Neural Network for Automatic Characterization of Lesions on 68Ga-PSMA PET/CT Images. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 951-954. IEEE 10.1109/EMBC.2019.8857955
Li, Hongwei; Reichert, Maximilian; Lin, Kanru; Tselousov, Nikita; Braren, Rickmer; Fu, Deliang; Schmid, Roland; Li, Ji; Menze, Bjoern; Shi, Kuangyu (2019). Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 2095-2098. IEEE 10.1109/EMBC.2019.8856745
Menze, Bjoern; Reyes, Mauricio; Van Leemput, Koen; Porz, Nicole; Wiest, Roland (2015). The Multimodal Brain TumorImage Segmentation Benchmark (BRATS). IEEE transactions on medical imaging, 34(10), pp. 199-2024. Institute of Electrical and Electronics Engineers IEEE 10.1109/TMI.2014.2377694
Bauer, Stefan; Tessier, Jean; Krieter, Oliver; Nolte, Lutz-P.; Reyes, Mauricio (2013). Integrated Spatio-Temporal Segmentation of Longitudinal Brain Tumor Imaging Studies. In: Menze, Bjoern; Langs, Georg; Montillo, Albert; Kelm, Michael; Müller, Henning; Tu, Zhuowen (eds.) Medical Computer Vision. Large Data in Medical Imaging. Lecture Notes in Computer Science: Vol. 8331 (pp. 74-83). Springer International Publishing 10.1007/978-3-319-05530-5_8
Li, Hongwei Bran; Conte, Gian Marco; Anwar, Syed Muhammad; Kofler, Florian; Ezhov, Ivan; van Leemput, Koen; Piraud, Marie; Diaz, Maria; Cole, Byrone; Calabrese, Evan; Rudie, Jeff; Meissen, Felix; Adewole, Maruf; Janas, Anastasia; Kazerooni, Anahita Fathi; LaBella, Dominic; Moawad, Ahmed W; Farahani, Keyvan; Eddy, James; Bergquist, Timothy; ... (28 June 2023). The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn). (arXiv). Cornell University
