Benedikt Wiestler

VLDB 2026 Research / reviewers in the wild / expert

dblp:165/7294 · also Bene Wiestler · DBLP profile ↗

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30ranked-venue papers

0as first author

24since 2021 · last 2026

0000-0002-2963-7772ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 23 · 17 since 2021Graphics, computer vision, multimedia, augmented reality and games · 16 · 11 since 2021Artificial intelligence and machine learning · 5 · 5 since 2021

Year	Publication	Venue	Position
2026	Beyond the LUMIR challenge: The pathway to foundational registration models Junyu Chen 0002, Shuwen Wei, Joel Honkamaa, Pekka Marttinen, Hang Zhang 0010, Min Liu 0008, Yichao Zhou 0002, Zuopeng Tan, Yi Wang 0028, Hongchao Zhou, Shunbo Hu, Yi Zhang 0120, Lukas Förner, Thomas Wendler 0001, Bailiang Jian, Benedikt Wiestler, Tim Hable, Dan Ruan, Frederic Madesta, Thilo Sentker, Wiebke Heyer, Lianrui Zuo, Yuwei Dai, Jerry L. Prince, Harrison X. Bai, Yong Du 0002, Yihao Liu 0003, Alessa Hering, Reuben Dorent, Lasse Hansen, Mattias P. Heinrich, Aaron Carass	Medical Image Anal.	18
2026	Improving out-of-domain generalization in Multiple Sclerosis detection and segmentation using Random Convolutions abstract Brain lesion segmentation is critical for diagnosing and monitoring neurological diseases such as Multiple Sclerosis (MS). However, lesion variability and differences in scanners and acquisition techniques pose a significant challenge to the robust generalization of automated segmentation models beyond their training domain. Traditional augmentations, such as rotation, intensity shifts, and scalings, often fail to capture the wide diversity observed across patient cases, limiting model generalizability. Random Convolutions (RC) address this limitation by introducing diverse intensity variations while preserving anatomical structures. Using an nnUNet-based model enhanced with RC augmentations, we achieved 5th place in the MSLesSeg challenge, highlighting that RC augmentations offer competitive in-domain performance. Building on this, we further assess model performance, both in terms of lesion detection and segmentation, in- and out-of-domain. We compare RC with several state-of-the-art augmentation and domain generalization strategies and show that an nnUNet trained with the RC augmentation is competitive in-domain and demonstrates superior generalization performance. • RC augmentation enhances out-of-domain performance in MS lesion detection and segmentation. • The model achieves a top-5 ranking in the MSLesSeg challenge with RC-enhanced nnUNet . • RC improves detection of small lesions, particularly under domain shift. • RC achieves optimal performance with mid-sized kernels ( k = 5–7) and moderate layer depths ( L = 6–8). • RC is a simple yet effective strategy for robust MS lesion segmentation. Aswathi Varma, Daniel Scholz 0005, Ayhan Can Erdur, Jan Peeken, Daniel Rueckert, Benedikt Wiestler	Pattern Recognit. Lett.	6
2026	Temporal Conditioning for Longitudinal Brain MRI Registration and Aging Analysis abstract Longitudinal brain analysis is essential for understanding healthy aging and identifying pathological deviations. Longitudinal registration of sequential brain MRI underpins such analyses. However, existing methods are limited by reliance on densely sampled time series, a trade-off between accuracy and temporal smoothness, and an inability to prospectively forecast future brain states. To overcome these challenges, we introduce TimeFlow, a learning-based framework for longitudinal brain MRI registration. TimeFlow uses a U-Net backbone with temporal conditioning to model neuroanatomy as a continuous function of age. Given only two scans from an individual, TimeFlow estimates accurate and temporally coherent deformation fields, enabling non-linear extrapolation to predict future brain states. This is achieved by our proposed inter-/extrapolation consistency constraints applied to both the deformation fields and deformed images. Remarkably, these constraints preserve temporal consistency and continuity without requiring explicit smoothness regularizers or densely sampled sequential data. Extensive experiments demonstrate that TimeFlow outperforms state-of-the-art methods in terms of both future timepoint forecasting and registration accuracy. Moreover, TimeFlow supports novel biological brain aging analyses by differentiating neurodegenerative trajectories from normal aging without requiring segmentation, thereby eliminating the need for labor-intensive annotations and mitigating segmentation inconsistency. TimeFlow offers an accurate, data-efficient, and annotation-free framework for longitudinal analysis of brain aging and chronic diseases, capable of forecasting brain changes beyond the observed study period. Bailiang Jian, Jiazhen Pan, Fabian Bongratz, Daniel Rueckert, Benedikt Wiestler, Christian Wachinger	IEEE Trans. Medical Imaging	7
2025	MM-DINOv2: Adapting Foundation Models for Multi-modal Medical Image Analysis Daniel Scholz 0005, Ayhan Can Erdur, Viktoria Ehm, Anke Meyer-Bäse, Jan Peeken, Daniel Rueckert, Benedikt Wiestler	MICCAI (8)	7
2025	Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method Daniel Scholz 0005, Ayhan Can Erdur, Robbie Holland, Viktoria Ehm, Jan Peeken, Benedikt Wiestler, Daniel Rueckert	MICCAI (6)	6
2025	NOVA: A Benchmark for Rare Anomaly Localization and Clinical Reasoning in Brain MRI abstract In many real-world applications, deployed models encounter inputs that differ from the data seen during training. Open-world recognition ensures that such systems remain robust as ever-emerging, previously _unknown_ categories appear and must be addressed without retraining.Foundation and vision-language models are pre-trained on large and diverse datasets with the expectation of broad generalization across domains, including medical imaging.However, benchmarking these models on test sets with only a few common outlier types silently collapses the evaluation back to a closed-set problem, masking failures on rare or truly novel conditions encountered in clinical use.We therefore present NOVA, a challenging, real-life _evaluation-only_ benchmark of $\sim$900 brain MRI scans that span 281 rare pathologies and heterogeneous acquisition protocols. Each case includes rich clinical narratives and double-blinded expert bounding-box annotations. Together, these enable joint assessment of anomaly localisation, visual captioning, and diagnostic reasoning. Because NOVA is never used for training, it serves as an _extreme_ stress-test of out-of-distribution generalisation: models must bridge a distribution gap both in sample appearance and in semantic space. Baseline results with leading vision-language models (GPT-4o, Gemini 2.0 Flash, and Qwen2.5-VL-72B) reveal substantial performance drops, with approximately a 65\% gap in localisation compared to natural-image benchmarks and 40\% and 20\% gaps in captioning and reasoning, respectively, compared to resident radiologists. Therefore, NOVA establishes a testbed for advancing models that can detect, localize, and reason about truly unknown anomalies. Cosmin Bercea, Philipp Raffler, Evamaria O. Riedel, Lena Schmitzer, Angela Kurz, Felix Bitzer, Paula Roßmüller, Julian Canisius, Mirjam L. Beyrle, Che Liu 0002, Wenjia Bai, Bernhard Kainz, Julia A. Schnabel, Benedikt Wiestler	NeurIPS	15
2025	Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans Ray Zirui Zhang, Ivan Ezhov, Michal Balcerak, Andy Zhu, Benedikt Wiestler, Bjoern Menze, John S. Lowengrub	Medical Image Anal.	5
2025	A Learnable Prior Improves Inverse Tumor Growth Modeling abstract Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95%. Jonas Weidner, Ivan Ezhov, Michal Balcerak, Marie Metz, Sergey Litvinov, Sebastian Kaltenbach, Leonhard F. Feiner, Laurin Lux, Florian Kofler, Jana Lipková, Jonas Latz, Daniel Rueckert, Bjoern Menze, Benedikt Wiestler	IEEE Trans. Medical Imaging	14
2024	H-ViT: A Hierarchical Vision Transformer for Deformable Image Registration abstract This paper introduces a novel top-down representation approach for deformable image registration, which estimates the deformation field by capturing various short-and long-range flow features at different scale levels. As a Hierarchical Vision Transformer (H- ViT), we propose a dual self-attention and cross-attention mechanism that uses high-level features in the deformation field to represent low-level ones, enabling information streams in the deformation field across all voxel patch embeddings irrespective of their spatial proximity. Since high-level features contain abstract flow patterns, such patterns are expected to effectively contribute to the representation of the deformation field in lower scales. When the self-attention module utilizes within-scale short-range patterns for representation, the cross-attention modules dynamically look for the key tokens across different scales to further interact with the local query voxel patches. Our method shows superior accuracy and visual quality over the state-of-the-art registration methods in five publicly available datasets, highlighting a substantial enhancement in the performance of medical imaging registration. The project link is available at https://mogvision.github.io/hvit. Morteza Ghahremani, Mohammad Khateri, Bailiang Jian, Benedikt Wiestler, Ehsan Adeli-Mosabbeb, Christian Wachinger	CVPR	4
2024	Diffusion Models with Implicit Guidance for Medical Anomaly Detection Cosmin Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel	MICCAI (11)	2
2024	Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization abstract Physical models in the form of partial differential equations serve as important priors for many under-constrained problems. One such application is tumor treatment planning, which relies on accurately estimating the spatial distribution of tumor cells within a patient’s anatomy. While medical imaging can detect the bulk of a tumor, it cannot capture the full extent of its spread, as low-concentration tumor cells often remain undetectable, particularly in glioblastoma, the most common primary brain tumor. Machine learning approaches struggle to estimate the complete tumor cell distribution due to a lack of appropriate training data. Consequently, most existing methods rely on physics-based simulations to generate anatomically and physiologically plausible estimations. However, these approaches face challenges with complex and unknown initial conditions and are constrained by overly rigid physical models. In this work, we introduce a novel method that integrates data-driven and physics-based cost functions, akin to Physics-Informed Neural Networks (PINNs). However, our approach parametrizes the solution directly on a dynamic discrete mesh, allowing for the effective modeling of complex biomechanical behaviors. Specifically, we propose a unique discretization scheme that quantifies how well the learned spatiotemporal distributions of tumor and brain tissues adhere to their respective growth and elasticity equations. This quantification acts as a regularization term, offering greater flexibility and improved integration of patient data compared to existing models. We demonstrate enhanced coverage of tumor recurrence areas using real-world data from a patient cohort, highlighting the potential of our method to improve model-driven treatment planning for glioblastoma in clinical practice. Michal Balcerak, Tamaz Amiranashvili, Jonas Weidner, Petr Karnakov, Johannes C. Paetzold, Ivan Ezhov, Petros Koumoutsakos, Benedikt Wiestler, Bjoern Menze	NeurIPS	9
2024	Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021 Carole H. Sudre, Kimberlin M. H. van Wijnen, Florian Dubost, Hieab Adams, David Atkinson, Frederik Barkhof, Mahlet A. Birhanu, Esther Bron, Robin Camarasa, Nish Chaturvedi, Qi Dou 0001, Tavia E. Evans, Ivan Ezhov, Haojun Gao, Marta Gironés-Sangüesa, Juan Domingo Gispert, Beatriz Gomez Anson, Alun D. Hughes, Mohammad Arfan Ikram, Silvia Ingala, Hans Rolf Jäger, Florian Kofler, Hugo J. Kuijf, Denis Kutnar, Bo Li 0088, Luigi Lorenzini, Bjoern Menze, José Luis Molinuevo, Yiwei Pan, Élodie Puybareau, Rafael Rehwald, Ruisheng Su, Lorna Smith, Therese Tillin, Guillaume Tochon, Hélène Urien, Bas H. M. van der Velden, Isabelle F. van der Velpen, Benedikt Wiestler, Frank J. Wolters, Pinar Yilmaz, Marius de Groot, Meike W. Vernooij, Marleen de Bruijne	Medical Image Anal.	44
2024	A Framework for Multimodal Medical Image Interaction abstract Medical doctors rely on images of the human anatomy, such as magnetic resonance imaging (MRI), to localize regions of interest in the patient during diagnosis and treatment. Despite advances in medical imaging technology, the information conveyance remains unimodal. This visual representation fails to capture the complexity of the real, multisensory interaction with human tissue. However, perceiving multimodal information about the patient's anatomy and disease in real-time is critical for the success of medical procedures and patient outcome. We introduce a Multimodal Medical Image Interaction (MMII) framework to allow medical experts a dynamic, audiovisual interaction with human tissue in three-dimensional space. In a virtual reality environment, the user receives physically informed audiovisual feedback to improve the spatial perception of anatomical structures. MMII uses a model-based sonification approach to generate sounds derived from the geometry and physical properties of tissue, thereby eliminating the need for hand-crafted sound design. Two user studies involving 34 general and nine clinical experts were conducted to evaluate the proposed interaction framework's learnability, usability, and accuracy. Our results showed excellent learnability of audiovisual correspondence as the rate of correct associations significantly improved (p < 0.001) over the course of the study. MMII resulted in superior brain tumor localization accuracy (p < 0.05) compared to conventional medical image interaction. Our findings substantiate the potential of this novel framework to enhance interaction with medical images, for example, during surgical procedures where immediate and precise feedback is needed. Laura Schütz, Sasan Matinfar, Gideon Schafroth, Navid Navab, Merle T. Fairhurst, Arthur Wagner, Benedikt Wiestler, Ulrich Eck, Nassir Navab	IEEE Trans. Vis. Comput. Graph.	7
2023	Why is the Winner the Best? abstract International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein	CVPR	114
2023	Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection Cosmin Bercea, Benedikt Wiestler, Daniel Rueckert, Julia A. Schnabel	MICCAI (5)	2
2023	Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations Julian McGinnis, Suprosanna Shit, Hongwei Li 0004, Vasiliki Sideri-Lampretsa, Robert Graf, Maik Dannecker, Jiazhen Pan, Nil Stolt Ansó, Mark Mühlau, Jan Kirschke, Daniel Rueckert, Benedikt Wiestler	MICCAI (8)	12
2023	Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images Chinmay Prabhakar, Hongwei Li 0004, Johannes C. Paetzold, Timo Löhr, Chen Niu, Mark Mühlau, Daniel Rueckert, Benedikt Wiestler, Bjoern Menze	MICCAI (8)	8
2023	The Liver Tumor Segmentation Benchmark (LiTS) abstract In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094. Patrick Bilic, Patrick Ferdinand Christ, Hongwei Li 0004, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland H. Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Amitai, Refael Vivanti, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Miriam Bellver, Lei Bi 0001, Hao Chen 0011, Grzegorz Chlebus, Erik Dam, Qi Dou 0001, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Grün, Xu Han 0009, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul F. Jaeger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz K. Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Xiaomeng Li 0001, John S. Lowengrub, Jun Ma 0016, Klaus H. Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Xiaojuan Qi 0001, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Daguang Xu, Xiaoping Yang 0001, Simon C. H. Yu, Yading Yuan, Miao Yue, Liping Zhang 0009, Manuel Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Joseph Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze	Medical Image Anal.	32
2023	Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling Ivan Ezhov, Kevin Scibilia, Katharina Franitza, Felix Steinbauer, Suprosanna Shit, Lucas Zimmer, Jana Lipková, Florian Kofler, Johannes C. Paetzold, Luca Canalini, Diana Waldmannstetter, Martin J. Menten, Marie Metz, Benedikt Wiestler, Bjoern Menze	Medical Image Anal.	14
2022	ROAM: Random layer mixup for semi-supervised learning in medical images abstract Abstract Medical image segmentation is one of the major challenges addressed by machine learning methods. However, these methods profoundly depend on a large amount of annotated data, which is expensive and time‐consuming. Semi‐supervised learning (SSL) approaches this by leveraging an abundant amount of unlabeled data. Recently, MixUp regularizer has been introduced to SSL methods by augmenting the model with new data points through linear interpolation at the input space. While this provides the model with new data, it is limited and may lead to inconsistent soft labels. It is argued that the linear interpolation at different representations provides the network with novel training signals and overcomes the inconsistency of the soft labels. This paper proposes ROAM as an SSL method that explores the manifold and performs linear interpolation on randomly selected layers to generate virtual data that has never been seen before, which encourages the network to be less confident for interpolated points. Hence it avoids overfitting, enhances the generalization, and shows less sensitivity to the domain shift. Extensive experiments are conducted on publicl datasets on whole‐brain and lung segmentation. ROAM achieves state‐of‐the‐art results in fully supervised (89.5%) and semi‐supervised (87.0%) settings with relative improvements up to 2.40% and 16.50%, respectively. Tariq M. Bdair, Benedikt Wiestler, Nassir Navab, Shadi Albarqouni	IET Image Process.	2
2022	Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models abstract Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highly-efficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of tumor modeling often imply solving an inverse problem, requiring up to tens of thousands of forward model evaluations when used for a Bayesian model personalization via sampling. This results in a total inference time prohibitively expensive for clinical translation. While recent data-driven approaches become capable of emulating physics simulation, they tend to fail in generalizing over the variability of the boundary conditions imposed by the patient-specific anatomy. In this paper, we propose a learnable surrogate for simulating tumor growth which maps the biophysical model parameters directly to simulation outputs, i.e. the local tumor cell densities, whilst respecting patient geometry. We test the neural solver in a Bayesian model personalization task for a cohort of glioma patients. Bayesian inference using the proposed surrogate yields estimates analogous to those obtained by solving the forward model with a regular numerical solver. The near real-time computation cost renders the proposed method suitable for clinical settings. The code is available at https://github.com/IvanEz/tumor-surrogate. Ivan Ezhov, Tudor Mot, Suprosanna Shit, Jana Lipková, Johannes C. Paetzold, Florian Kofler, Chantal Pellegrini, Marcel Kollovieh, Fernando Navarro, Hongwei Li 0004, Marie Metz, Benedikt Wiestler, Bjoern Menze	IEEE Trans. Medical Imaging	12
2021	Imbalance-Aware Self-supervised Learning for 3D Radiomic Representations Hongwei Li 0004, Fei-Fei Xue, Krishna Chaitanya, Shengda Luo, Ivan Ezhov, Benedikt Wiestler, Jianguo Zhang 0001, Bjoern Menze	MICCAI (2)	6
2021	Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study Christoph Baur, Stefan Denner, Benedikt Wiestler, Nassir Navab, Shadi Albarqouni	Medical Image Anal.	3
2021	VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images Anjany Sekuboyina, Malek El Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li 0004, Giles Tetteh, Jan Kukacka, Christian Payer, Darko Stern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Leßmann, Yujin Hu, Tianfu Wang 0001, Dong Yang 0005, Daguang Xu, Felix Ambellan, Tamaz Amiranashvili, Moritz Ehlke, Hans Lamecker, Sebastian Lehnert, Marilia Lirio, Nicolás Pérez de Olaguer, Heiko Ramm, Manish Sahu, Alexander Tack, Stefan Zachow, Xinjun Ma, Christoph Angerman, Xin Wang 0113, Alexandre Kirszenberg, Élodie Puybareau, Yiwei Bai, Brandon H. Rapazzo, Timyoas Yeah, Amber Zhang, Shangliang Xu, Feng Hou, Zhiqiang He 0002, Chan Zeng, Zheng Xiangshang, Xu Liming, Tucker J. Netherton, Raymond P. Mumme, Laurence E. Court, Zixun Huang, Chenhang He, Li-Wen Wang, Sai-Ho Ling, Lê Duy Huynh, Nicolas Boutry, Roman Jakubícek, Jirí Chmelík, Supriti Mulay, Mohanasankar Sivaprakasam, Johannes C. Paetzold, Suprosanna Shit, Ivan Ezhov, Benedikt Wiestler, Ben Glocker, Alexander Valentinitsch, Markus Rempfler, Bjoern Menze, Jan Kirschke	Medical Image Anal.	64
2020	SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI Christoph Baur, Robert Graf, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab	MICCAI (2)	3
2020	Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab	MICCAI (4)	2
2019	Graph Convolution Based Attention Model for Personalized Disease Prediction Anees Kazi, Shayan Shekarforoush, S. Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Benedikt Wiestler, Karsten Kortüm, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab	MICCAI (4)	6
2019	DiamondGAN: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis Hongwei Li 0004, Johannes C. Paetzold, Anjany Sekuboyina, Florian Kofler, Jianguo Zhang 0001, Jan Kirschke, Benedikt Wiestler, Bjoern Menze	MICCAI (4)	7
2019	Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference abstract Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high-resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in GBM patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and, thus, is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design. Jana Lipková, Panagiotis Angelikopoulos, Stephen Wu 0001, Esther Alberts, Benedikt Wiestler, Christian Diehl, Christine Preibisch, Thomas Pyka, Stephanie Combs, Panagiotis Hadjidoukas, Koenraad Van Leemput, Petros Koumoutsakos, John S. Lowengrub, Bjoern Menze	IEEE Trans. Medical Imaging	5
2018	Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases Miguel Molina-Romero, Benedikt Wiestler, Pedro A. Gómez, Marion I. Menzel, Bjoern Menze	MICCAI (3)	2