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Fabian Isensee

7 accepted papers

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
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

Quality Assured: Rethinking Annotation Strategies in Imaging AI

ECCV 2024poster

"[width=1]figures/fig1l owr es.png Figure 1: Research Questions (RQs) tackled in this work. Based on 57,648 instance segmentation masks annotated by 924 annotators and 34 quality assurance (QA) workers from five different annotation providers, we (1) compared the effectiveness of generating high-qua…

Cited by 4SourcePDFScholar
2024

Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures

ECCV 2024poster

"Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation loss functions, such as Dice or Cross-Entropy, focus on volumetric overlap, often at the expense of preserving structural…

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…

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
2021

GP-ConvCNP: Better generalization for conditional convolutional Neural Processes on time series data

UAI 2021poster

Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points. A recent addition to this family, Convolutional Conditional Neural Proce…