Lucas Zimmer

According to our database, Lucas Zimmer authored at least 13 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A Biophysically-Conditioned Generative Framework for 3D Brain Tumor MRI Synthesis.

Valentin Biller, Lucas Zimmer, Can Erdur, Sandeep Nagar, Daniel Rückert, Niklas Bubeck, Jonas Weidner

CoRR, October, 2025

PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma.

Lucas Zimmer, Jonas Weidner, Michal Balcerak, Florian Kofler, Ivan Ezhov, Bjoern H. Menze, Benedikt Wiestler

CoRR, September, 2025

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis.

Florian Kofler, Marcel Rosier, Mehdi Astaraki, Hendrik Kristian Möller, Isra Mekki, Josef A. Buchner, Anton Schmick, Arianna Pfiffer, Eva Oswald, Lucas Zimmer, Ezequiel de la Rosa, Sarthak Pati, Julian Canisius, Arianna Piffer, Ujjwal Baid, Mahyar Valizadeh, Akis Linardos, Jan C. Peeken, Surprosanna Shit, Felix Steinbauer, Daniel Rueckert, Rolf A. Heckemann, Spyridon Bakas, Jan Kirschke, Constantin von See, Ivan Ezhov, Marie Piraud, Benedikt Wiestler, Bjoern H. Menze

CoRR, July, 2025

A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration.

Jonas Weidner, Michal Balcerak, Ivan Ezhov, André Datchev, Laurin Lux, Lucas Zimmer, Daniel Rueckert, Bjoern H. Menze, Benedikt Wiestler

Proceedings of the Computational Mathematics Modeling in Cancer Analysis, 2025

2024

Spatial Brain Tumor Concentration Estimation for Individualized Radiotherapy Planning.

Jonas Weidner, Michal Balcerak, Ivan Ezhov, André Datchev, Laurin Lux, Lucas Zimmer, Daniel Rückert, Bjoern H. Menze, Benedikt Wiestler

CoRR, 2024

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge.

Hongwei Bran Li, Fernando Navarro, Ivan Ezhov, Amirhossein Bayat, Dhritiman Das, Florian Kofler, Suprosanna Shit, Diana Waldmannstetter, Johannes C. Paetzold, Xiaobin Hu, Benedikt Wiestler, Lucas Zimmer, Tamaz Amiranashvili, Chinmay Prabhakar, Christoph Berger, Jonas Weidner, Michelle Alonso-Basanta, Arif Rashid, Ujjwal Baid, Wesam Adel, Deniz Alis, Bhakti Baheti, Yingbin Bai, Ishaan Bhat, Sabri Can Cetindag, Wenting Chen, Li Cheng, Prasad Dutande, Lara Dular, Mustafa A. Elattar, Ming Feng, Shengbo Gao, Henkjan Huisman, Weifeng Hu, Shubham Innani, Wei Jiat, Davood Karimi, Hugo J. Kuijf, Jin Tae Kwak, Hoang Long Le, Xiang Lia, Huiyan Lin, Tongliang Liu, Jun Ma, Kai Ma, Ting Ma, Ilkay Öksüz, Robbie Holland, Arlindo L. Oliveira, Jimut Bahan Pal, Xuan Pei, Maoying Qiao, Anindo Saha, Raghavendra Selvan, Linlin Shen, João Lourenço Silva, Ziga Spiclin, Sanjay N. Talbar, Dadong Wang, Wei Wang, Xiong Wang, Yin Wang, Ruiling Xia, Kele Xu, Yanwu Yan, Mert Yergin, Shuang Yu, Lingxi Zeng, YingLin Zhang, Jiachen Zhao, Yefeng Zheng, Martin Zukovec, Richard K. G. Do, Anton S. Becker, Amber L. Simpson, Ender Konukoglu, András Jakab, Spyridon Bakas, Leo Joskowicz, Bjoern H. Menze

CoRR, 2024

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 H. Menze

Medical Image Anal., 2023

2022

Casting the inverse problem as a database query. The case of personalized tumor growth modeling.

Ivan Ezhov, Marcel Rosier, Lucas Zimmer, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Kevin Scibilia, Leon Mächler, Katharina Franitza, Tamaz Amiranashvili, Marie Metz, Sailesh Conjeti, Benedikt Wiestler, Bjoern H. Menze

CoRR, 2022

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling.

Ivan Ezhov, Marcel Rosier, Lucas Zimmer, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Kevin Scibilia, Felix Steinbauer, Leon Mächler, Katharina Franitza, Tamaz Amiranashvili, Martin J. Menten, Marie Metz, Sailesh Conjeti, Benedikt Wiestler, Bjoern H. Menze

Proceedings of the Machine Learning for Health, 2022

Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks.

Arber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, Frank Hutter

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.

Lucas Zimmer, Marius Lindauer, Frank Hutter

IEEE Trans. Pattern Anal. Mach. Intell., 2021

2020

NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search.

Julien Siems, Lucas Zimmer, Arber Zela, Jovita Lukasik, Margret Keuper, Frank Hutter

CoRR, 2020

Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.

Lucas Zimmer, Marius Lindauer, Frank Hutter

CoRR, 2020