Hannover Medical School

Medical imaging foundation model unifying pathology and radiology, serving classification and segmentation on 2D, 3D and gigapixel inputs.

Whole-slide histopathology foundation model trained end-to-end on slide-level labels across 18 tasks, on 5% of the energy of SSL-trained peers.

Histopathology tile encoder trained by supervised multi-task learning over 16 annotated tasks, matching self-supervised encoders on 6% of patches.

Multi-task pretrained biomedical imaging model whose frozen features match ImageNet fine-tuning on CT, X-ray and histology tasks from 1% of labels.