Lucas Zimmer

Orcid: 0000-0002-5167-2929

According to our database1, Lucas Zimmer authored at least 15 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growth.
CoRR, March, 2026

2025
PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma.
CoRR, September, 2025

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis.
CoRR, July, 2025

From Fiber Tracts to Tumor Spread: Biophysical Modeling of Butterfly Glioma Growth Using Diffusion Tensor Imaging.
Proceedings of the Computational Diffusion MRI - 16th International Workshop, 2025

A Biophysically-Conditioned Generative Framework for 3D Brain Tumor MRI Synthesis.
Proceedings of the Segmentation, Classification, and Synthesis for Brain Tumors and Traumatic Brain Injuries, 2025

A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration.
Proceedings of the Computational Mathematics Modeling in Cancer Analysis, 2025

2024
Spatial Brain Tumor Concentration Estimation for Individualized Radiotherapy Planning.
CoRR, 2024

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge.
CoRR, 2024

2023
Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling.
Medical Image Anal., 2023

2022
Casting the inverse problem as a database query. The case of personalized tumor growth modeling.
CoRR, 2022

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling.
Proceedings of the Machine Learning for Health, 2022

Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

2020
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search.
CoRR, 2020

Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.
CoRR, 2020


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