works on

From the 1 of 9 linked papers with an AI index.

most citedPrimus: Enforcing Attention Usage for 3D Medical Image Segmentation

1 citations · 1 across the 2 of their papers we have counts for

collaborators

9 papers

cs.LG2026

Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI

Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati +7

The paper proposes a federated learning framework that adjusts differential privacy noise based on each healthcare institution's compliance level, allowing lower‑compliance sites t…

cs.CV20261 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…

eess.IV2025

MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentation

Saikat Roy, Yannick Kirchhoff, Constantin Ulrich +4

Large-scale supervised pretraining is rapidly reshaping 3D medical image segmentation. However, existing efforts focus primarily on increasing dataset size and overlook the questio…

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

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.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…