cozygene/SLIViT

Generalist neuroimaging models from health system learning [Nat Med '26]

BiomedParse: A Foundation Model for Joint Segmentation, Detection, and Recognition of Biomedical Objects Across Nine Modalities

[ICLR 2025] Learning General-purpose Biomedical Volume Representations using Randomized Synthesis

Simplifying deep learning for medical imaging

LesionLocator is a framework for zero-shot lesion segmentation and longitudinal tumor tracking in 3D full-body imaging.

A multi-agent framework for autonomous end-to-end development of radiomics and deep learning models for medical imaging

SAM-Med3D: An Efficient General-purpose Promptable Segmentation Model for 3D Volumetric Medical Image

AI Toolkit for Healthcare Imaging

[MICCAI 2019 Young Scientist Award] [MedIA Best Paper Award] Models Genesis: self-supervised pre-training for 3D medical images. Learns transferable representations from unlabeled CT and MRI volumes, then fine-tunes for downstream segmentation and classification. Keras and PyTorch weights included.