Lena Maier-hein — accepted AI-conference papers

2026

CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis

Alexander Baumann, Leonardo Ayala, Silvia Seidlitz, Jan Sellner, Alexander Studier-Fischer, Berkin Özdemir +2

ICLR 2026poster

Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote sensing. However, variability in channel dimensionality and captured wavelengths among spectral cameras impede the deve…

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2024

FISBe: A Real-World Benchmark Dataset for Instance Segmentation of Long-Range Thin Filamentous Structures

Lisa Mais, Peter Hirsch, Claire Managan, Ramya Kandarpa, Josef Lorenz Rumberger, Annika Reinke +3

CVPR 2024poster

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extrem…

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2024

Overcoming Common Flaws in the Evaluation of Selective Classification Systems

Jeremias Traub, Till J. Bungert, Carsten T. Lüth, Michael Baumgartner, Klaus Maier-Hein, Lena Maier-hein +1

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…

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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 29 Source ↗PDF ↗Scholar ↗

2015

The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms

Katrin Honauer, Lena Maier-Hein, Daniel Kondermann

ICCV 2015poster

Performance characterization of stereo methods is mandatory to decide which algorithm is useful for which application. Prevalent benchmarks mainly use the root mean squared error (RMS) with respect to ground truth disparity maps to quantify algorithm performance. We show that the RMS is of limited…

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