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Bjoern Menze

15 accepted papers

2026

Beyond Uniformity: Regularizing Implicit Neural Representations through a Lipschitz Lens

Julian McGinnis, Suprosanna Shit, Florian A. Hölzl, Paul Friedrich, Paul Büschl, Vasiliki Sideri-Lampretsa +5
ICLR 2026poster

Implicit Neural Representations (INRs) have shown great promise in solving inverse problems, but their lack of inherent regularization often leads to a trade-off between expressiveness and smoothness. While Lipschitz continuity presents a principled form of implicit regularization, it is often appli…

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2026

Optimizing Rank for High-Fidelity Implicit Neural Representations

Julian McGinnis, Florian A. Hölzl, Suprosanna Shit, Florentin Bieder, Paul Friedrich, Mark Mühlau +3
ICML 2026poster

Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-frequency content. This has directed research efforts towards architectural interventions, such as coordinate embeddings or specialized activation functions…

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2025

Better Tokens for Better 3D: Advancing Vision-Language Modeling in 3D Medical Imaging

Ibrahim Ethem Hamamci, Sezgin Er, Suprosanna Shit, Hadrien Reynaud, Dong Yang, Pengfei Guo +4
NeurIPS 2025poster

Recent progress in vision-language modeling for 3D medical imaging has been fueled by large-scale computed tomography (CT) corpora with paired free-text reports, stronger architectures, and powerful pretrained models. This has enabled applications such as automated report generation and text-conditi…

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2025

Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling

Michal Balcerak, Tamaz Amiranashvili, Antonio Terpin, Suprosanna Shit, Lea Bogensperger, Sebastian Kaltenbach +2
NeurIPS 2025poster

Current state-of-the-art generative models map noise to data distributions by matching flows or scores. A key limitation of these models is their inability to readily integrate available partial observations and additional priors. In contrast, energy-based models (EBMs) address this by incorporating…

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2025

RadGPT: Constructing 3D Image-Text Tumor Datasets

Pedro R.A.S. Bassi, Mehmet Can Yavuz, Ibrahim Ethem Hamamci, Sezgin Er, Xiaoxi Chen, Wenxuan Li +7
ICCV 2025poster

Cancers identified in CT scans are usually accompanied by detailed radiology reports, but publicly available CT datasets often lack these essential reports. This absence limits their usefulness for developing accurate report generation AI. To address this gap, we present AbdomenAtlas 3.0, the first…

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2025

vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation

Bastian Wittmann, Yannick Wattenberg, Tamaz Amiranashvili, Suprosanna Shit, Bjoern Menze
CVPR 2025poster

Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular patterns and scales, signal-to-noise ratios, and background tissues. These variations, along with domain gaps arising from…

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2024

GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

Ibrahim Ethem Hamamci*, Sezgin Er, Anjany Sekuboyina, Enis Simsar, Alperen Tezcan, Ayse Gulnihan Simsek +10
ECCV 2024poster

"Text-conditional medical image generation is vital for radiology, augmenting small datasets, preserving data privacy, and enabling patient-specific modeling. However, its applications in 3D medical imaging, such as CT and MRI, which are crucial for critical care, remain unexplored. In this paper, w…

Cited by 24Source ↗PDF ↗code ↗Scholar ↗
2024

Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization

Michal Balcerak, Tamaz Amiranashvili, Andreas Wagner, Jonas Weidner, Petr Karnakov, Johannes C. Paetzold +4
NeurIPS 2024poster

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 imagi…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2023

Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes

Nico Daniel Stucki, Johannes C. Paetzold, Suprosanna Shit, bjoern menze, Ulrich Bauer
ICML 2023poster

Segmentation models predominantly optimize pixel-overlap-based loss, an objective that is actually inadequate for many segmentation tasks. In recent years, their limitations fueled a growing interest in topology-aware methods, which aim to recover the topology of the segmented structures. However, s…

Cited by 40Source ↗PDF ↗code ↗Scholar ↗
2023

Why Is the Winner the Best?

Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu D. Tizabi, Fabian Isensee, Tim J. Adler +119
CVPR 2023poster

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…

Cited by 29Source ↗PDF ↗Scholar ↗
2022

Relationformer: A Unified Framework for Image-to-Graph Generation

Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes Paetzold, Ivan Ezhov, Hongwei Li +5
ECCV 2022poster

"A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blood-vessel network extraction, or scene graph generation. Traditionally, image-to-graph generation is addressed with a t…

Cited by 69Source ↗PDF ↗code ↗Scholar ↗
2021

Pyramid Architecture Search for Real-Time Image Deblurring

Xiaobin Hu, Wenqi Ren, Kaicheng Yu, Kaihao Zhang, Xiaochun Cao, Wei Liu +1
ICCV 2021poster

Multi-scale and multi-patch deep models have been shown effective in removing blurs of dynamic scenes. However, these methods still have one major obstacle: manually designing a lightweight and high-efficiency network is challenging and time-consuming. To tackle this problem, we propose a novel debl…

Cited by 48PDF ↗Scholar ↗
2021

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience

Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar +6
NeurIPS 2021poster

Biological neural networks define the brain function and intelligence of humans and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal organization is closely interconnected with the spatial organization of the brain's microvasculature, which supplies oxygen to the neuro…

Cited by 28Source ↗Scholar ↗
2020

Face Super-Resolution Guided by 3D Facial Priors

Xiaobin Hu, Wenqi Ren, John LaMaster, Xiaochun Cao, Xiaoming Li, Zechao Li +2
ECCV 2020poster

State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high-resolution facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and str…

Cited by 85Source ↗PDF ↗Scholar ↗
2020

Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution

Yu Zhao, Fan Yang, Yuqi Fang, Hailing Liu, Niyun Zhou, Jun Zhang +5
CVPR 2020poster

Multiple instance learning (MIL) is a typical weakly-supervised learning method where the label is associated with a bag of instances instead of a single instance. Despite extensive research over past years, effectively deploying MIL remains an open and challenging problem, especially when the commo…

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