Nick Lemke

SG-NCA enables accurate, privacy-preserving surgical scene understanding on fanless edge devices with performance comparable to larger models while using 55× fewer parameters.

TheODen enables secure, firewall-compatible federated learning for histopathology, demonstrating robust cancer segmentation across multiple German hospitals without sharing sensitive data or opening client-side ports.

UNEG, a multi-model benchmark for continual learning in medical image segmentation, outperforms existing methods by maintaining separate networks for each training stage and using reconstruction error to select the appropriate model during inference.

FedNCA enables secure, low-cost federated medical image segmentation on resource-constrained edge devices, helping overcome computing, connectivity, and privacy barriers to AI healthcare adoption in LMICs.

OctreeNCA enables fast, globally consistent segmentation of extremely large medical images and videos at once, using about 90% less GPU memory than a UNet.

A distribution-aware replay strategy for medical image segmentation mitigates forgetting through feature auto-encoding while detecting model failure from out-of-distribution instances, addressing privacy concerns and unexpected distribution shifts.