Efficient Pan-Cancer Lesion Segmentation from Partially Labeled Data with nnU-Net. - Yannick Kirchhoff; Maximilian Rokuss; Benjamin Hamm; Ashis Ravindran; Constantin Ulrich; Klaus H. Maier-Hein; Fabian Isensee (FLARE@MICCAI, 2024)

Efficient Cross-Modality Abdominal Organ Segmentation Using nnU-Net and MIND Descriptors. - Yannick Kirchhoff; Maximilian Rokuss; Benjamin Hamm; Ashis Ravindran; Constantin Ulrich; Klaus H. Maier-Hein; Fabian Isensee (FLARE@MICCAI, 2024)

Optimizing nnU-Net with OpenVINO for Fast CPU Inference in Abdominal Organ Segmentation. - Yannick Kirchhoff; Ashis Ravindran; Maximilian Rokuss; Benjamin Hamm; Constantin Ulrich; Klaus H. Maier-Hein; Fabian Isensee (FLARE@MICCAI, 2024)

Abstract: Client Security Alone Fails in Federated Learning - 2D and 3D Attack Insights. - Santhosh Parampottupadam; Ralf Floca; Dimitrios Bounias; Benjamin Hamm; Saikat Roy; Sinem Sav; Maximilian Zenk; Klaus H. Maier-Hein (Bildverarbeitung für die Medizin, 2025)

nnInteractive: Redefining 3D Promptable Segmentation. - Fabian Isensee; Maximilian Rokuss; Lars Krämer; Stefan Dinkelacker; Ashis Ravindran; Florian Stritzke; Benjamin Hamm; Tassilo Wald; Moritz Langenberg; Constantin Ulrich; Jonathan Deissler; Ralf Floca; et al. (CoRR, 2025)

Inclusive, Differentially Private Federated Learning for Clinical Data. - Santhosh Parampottupadam; Melih Cosgun; Sarthak Pati; Maximilian Zenk; Saikat Roy; Dimitrios Bounias; Benjamin Hamm; Sinem Sav; Ralf Floca; Klaus H. Maier-Hein (CoRR, 2025)

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation. - Maximilian Rokuss; Benjamin Hamm; Yannick Kirchhoff; Klaus H. Maier-Hein (CoRR, 2025)