Project-MONAI / Project-MONAI/MONAILabel

[HAI] Challenge scoping for outreach (see #1905)

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Human-AI Interaction
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Python
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Description

This is a discussion/paper-trail ticket documenting one specific line of outreach under #[main ticket number]'s general call — not a feature request in its own right.

Context:

HAIG scoped which existing biomedical segmentation challenges to approach about interactive-segmentation collaboration, as part of standardising interactive-segmentation evaluation practice.

Method:

~5-year scan (2021–2026), ranked primarily on feasibility of actually hopping onto an existing setup (recurring + currently-active organising team as a hard filter, plus existing team ties, and a real-clinician-annotation angle where relevant), and secondarily on alignment with the under-tested axes named in Parhom's eval pipeline talk. Radiological imaging as primary scope on top of both.
Current shortlist (top → bottom): CVPR SEGFM3D, TopCoW/TopBrain, ToothFairy3, autoPET, PENGWIN, HECKTOR, ISLES, SegRap, FLARE. Full reasoning: see the shared doc: https://docs.google.com/document/d/1QhO0eJiVtWGpYzWAZioRmnvb0V1xyXBieBDggL7RyTY/edit?usp=sharing

Why post this here: creating a public paper trail within the MONAI project, given the overlap between HAIG's goals and MONAI's interactive-segmentation tooling (MONAI Label).

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with issue #1905 and the linked shared document to understand the outreach context and shortlist. Review how this paper trail relates to MONAI Label's interactive-segmentation tooling; done means the scope, ranking, and rationale are recorded publicly in the MONAI project.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
documentation
Issue type
Documentation
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
25/100

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