# Project-MONAI/research-contributions

Implementations of recent research prototypes/demonstrations using MONAI.

Repository: https://github.com/Project-MONAI/research-contributions
Canonical: https://ross.abutalabs.com/products/research-contributions
Homepage: https://project-monai.github.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: monai, monai-components, paper
Last push: 2025-10-06T22:41:24+00:00

## Health v2 (maintenance only)
Score: 43/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 45, release rhythm 8, longevity 100
- inputs: {"age_days": 2176, "days_push": 331, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1220, forks 389 (observed 2026-08-28T04:04:01.891360+00:00)

## What it is
A collection of peer-reviewed research prototype implementations built on the MONAI framework for medical imaging AI. It serves as a fast-track showcase for cutting-edge MONAI-based research that may later be integrated into the core MONAI components.

## Use cases
- reproduce results from published medical imaging papers
- try state-of-the-art 3D segmentation models before they land in MONAI core
- build medical imaging deep learning prototypes on PyTorch
- generate synthetic CT images with research models
- experiment with transformer-based 3D medical image architectures
- contribute peer-reviewed research code to the MONAI ecosystem

## When to choose
- you want one-click reproducible implementations of recent published medical imaging research
- you need cutting-edge MONAI-based models not yet available in the stable core library
- you are a researcher demonstrating a new medical imaging method built on MONAI components

## When to avoid
- you need production-grade, fully maintained code with strict quality guarantees
- you want stable APIs guaranteed compatible across MONAI releases
- you need general-purpose deep learning outside the medical imaging domain

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, data-science
- domain: deep-learning, healthcare, machine-learning
- platform: python, cross-platform
- tags: medical-imaging, pytorch, research-prototypes, segmentation, peer-reviewed, monai, 3d-imaging, reproducibility, research, gpu

## Member repositories
- Project-MONAI/research-contributions (main) score 43

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:01.891360+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:15:55.945727+00:00, confidence not recorded.
  - readme: https://github.com/Project-MONAI/research-contributions (fetched 2026-08-28T04:04:01.891360+00:00, sha 0f40e325c795)
  - homepage: https://project-monai.github.io/ (fetched 2026-08-29T12:24:31.047564+00:00, sha 3a12c6d3df1d)
- Data as of 2026-08-30T08:39:29.467469+00:00.