most citednnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation

22 citations · 28 across the 12 of their papers we have counts for

collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV2025

CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series

Nico Albert Disch, Saikat Roy, Constantin Ulrich +5

Forecasting how 3D medical scans evolve over time is important for disease progression, treatment planning, and developmental assessment. Yet existing models either rely on a singl…

cs.CV2025

Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging

Nico Albert Disch, Yannick Kirchhoff, Robin Peretzke +5

Understanding temporal dynamics in medical imaging is crucial for applications such as disease progression modeling, treatment planning and anatomical development tracking. However…

cs.CV2025★ 1 cited

Primus: Enforcing Attention Usage for 3D Medical Image Segmentation

Tassilo Wald, Saikat Roy, Fabian Isensee +7

Transformers have achieved remarkable success across multiple fields, yet their impact on 3D medical image segmentation remains limited with convolutional networks still dominating…

cs.CV2025★ 2 cited

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

Maximilian Rokuss, Yannick Kirchhoff, Seval Akbal +7

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 capab…

cs.CV2025

Expectation-Maximization as the Engine of Scalable Medical Intelligence

Wenxuan Li, Pedro R. A. S. Bassi, Tianyu Lin +19

Large, high-quality, annotated datasets are the foundation of medical AI research, but constructing even a small, moderate-quality, annotated dataset can take years of effort from…

cs.CV2024★ 2 cited

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

Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50

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…

cs.CV2024★ 22 cited

nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation

Fabian Isensee, Tassilo Wald, Constantin Ulrich +4

The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art re…