Project MONAI's Apache-2.0 PyTorch framework for medical image segmentation, classification, and registration — the standard for AI radiology research.

Allen AI's Apache-2.0 spaCy models for biomedical text with UMLS, MeSH, GO, HPO, and RxNorm entity linking and NER for genes, diseases, chemicals, and proteins.

DKFZ's Apache-2.0 framework that automatically configures a U-Net for any medical image segmentation dataset — state-of-the-art results with zero manual tuning.

MIT-licensed deep learning framework for drug discovery and computational biology — molecular property prediction, virtual screening, and ADMET modelling.

Microsoft's MIT-licensed GPT trained on 15M PubMed abstracts for biomedical relation extraction, question answering, and literature text generation.

MIT-licensed spaCy extension for clinical text processing — section detection, clinical concept extraction, negation, temporality, and UMLS entity linking.

MIT-licensed BERT fine-tuned on MIMIC-III clinical notes for superior clinical embeddings — NER, relation extraction, and ICU outcome prediction.

Python library for ML on brain imaging data: fMRI decoding, connectivity analysis, surface plotting.

Apache-2.0 unsupervised clinical NER and linking library that maps free-text clinical mentions to SNOMED CT, ICD-10, UMLS, and other biomedical ontologies.

Python dataflow engine that wraps FSL, SPM, FreeSurfer, ANTs, and AFNI into reproducible neuroimaging pipelines.

Active-learning annotation server for medical images: auto-segmentation, interactive refinement, OHIF/3D Slicer integration.