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Klaus Maier-Hein

13 accepted papers

2026

Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performance

CVPR 2026

Uncertainty Quantification (UQ) is crucial for ensuring the reliability of automated image segmentations in safety-critical domains like biomedical image analysis or autonomous driving. In segmentation, UQ generates pixel-wise uncertainty scores that must be aggregated into image-level scores for do

Cited by 0SourcecodeScholar
2026

CRONOS: Continuous time reconstruction for 4D medical longitudinal series

ICLR 2026poster

Forecasting how 3D medical scans evolve along time is important for disease progression, treatment planning, and developmental assessment. Yet existing models either rely on a single prior scan, fixed grid times, or target global labels, which limits voxel-level forecasting under irregular sampling.…

Cited by 0SourcecodeScholar
2026

VoxTell: Free-Text Promptable Universal 3D Medical Image Segmentation

CVPR 2026

We introduce VoxTell, a vision-language model for text-prompted volumetric medical image segmentation. It maps free-form descriptions, from single words to full clinical sentences, to 3D masks. Trained on 62K+ CT, MRI, and PET volumes spanning 1K+ anatomical and pathological classes, VoxTell uses mu

Cited by 0SourcecodeScholar
2025

An OpenMind for 3D Medical Vision Self-supervised Learning

ICCV 2025poster

The field of self-supervised learning (SSL) for 3D medical images lacks consistency and standardization. While many methods have been developed, it is impossible to identify the current state-of-the-art, due to i) varying and small pretraining datasets, ii) varying architectures, and iii) being eval…

2025

LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging

CVPR 2025poster

In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing the first end-to-end model capable of 4D tracking with dense spatial prompts. Our model leverages an extensive dataset of 23,262 annotated medical scans…

2025

ReSi: A Comprehensive Benchmark for Representational Similarity Measures

ICLR 2025poster

Measuring the similarity of different representations of neural architectures is a fundamental task and an open research challenge for the machine learning community. This paper presents the first comprehensive benchmark for evaluating representational similarity measures based on well-defined groun…

2025

Revisiting MAE Pre-training for 3D Medical Image Segmentation

CVPR 2025highlight

Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from the scarcity of labeled data. While SSL has revolutionized fields like natural language processing and computer vision, it…

Cited by 5SourcePDFScholar
2024

Decoupling Semantic Similarity from Spatial Alignment for Neural Networks.

NeurIPS 2024poster

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity…

2024

Overcoming Common Flaws in the Evaluation of Selective Classification Systems

NeurIPS 2024spotlight

Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these systems typically assumes fixed working points based…

2024

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

NeurIPS 2024poster

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks…

2024

ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation

ICLR 2024oral

Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, the field is currently hampered by a gap bet…

2023

Why Is the Winner the Best?

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 29SourcePDFScholar
2018

A Probabilistic U-Net for Segmentation of Ambiguous Images

NeurIPS 2018spotlight

Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations. We consider the t…