RRepoGEO

REPOGEO REPORT · LITE

JunMa11/MICCAI-OpenSourcePapers

Default branch master · commit 86491a35 · scanned 5/9/2026, 3:07:23 AM

GitHub: 1,293 stars · 229 forks

AI VISIBILITY SCORE
15 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface JunMa11/MICCAI-OpenSourcePapers, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify README's opening sentence to emphasize 'curated list'

    Why:

    COPY-PASTE FIX
    This repository provides a comprehensive, curated list of open-source papers and their associated code from the MICCAI conferences (2019-2023), serving as a central resource for researchers in medical image computing.
  • mediumtopics#2
    Add topics describing the repo as a collection/resource

    Why:

    CURRENT
    deep-learning, medical-imaging
    COPY-PASTE FIX
    deep-learning, medical-imaging, paper-list, code-collection, research-resource, miccai
  • lowhomepage#3
    Add a homepage link to the MICCAI conference proceedings

    Why:

    COPY-PASTE FIX
    https://www.miccai2023.org/

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface JunMa11/MICCAI-OpenSourcePapers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Project-MONAI/MONAI
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. Project-MONAI/MONAI · recommended 3×
  2. MONAI · recommended 1×
  3. nnU-Net · recommended 1×
  4. AlphaFold · recommended 1×
  5. TensorFlow Models · recommended 1×
  • CATEGORY QUERY
    Where can I find open-source deep learning implementations for medical image analysis?
    you: not recommended
    AI recommended (in order):
    1. MONAI
    2. nnU-Net
    3. AlphaFold
    4. TensorFlow Models
    5. Keras Applications
    6. PyTorch Hub
    7. GitHub Search

    AI recommended 7 alternatives but never named JunMa11/MICCAI-OpenSourcePapers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest open-source deep learning models for medical image segmentation tasks?
    you: not recommended
    AI recommended (in order):
    1. nnU-Net (MIC-DKFZ/nnUNet)
    2. MONAI (Project-MONAI/MONAI)
    3. Swin UNETR (Project-MONAI/MONAI)
    4. UNETR (Project-MONAI/MONAI)
    5. TransUNet (Beckschen/TransUNet)
    6. DeepLabV3+
    7. MedNeXt (MIC-DKFZ/MedNeXt)

    AI recommended 7 alternatives but never named JunMa11/MICCAI-OpenSourcePapers. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of JunMa11/MICCAI-OpenSourcePapers?
    pass
    AI did not name JunMa11/MICCAI-OpenSourcePapers — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts JunMa11/MICCAI-OpenSourcePapers in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name JunMa11/MICCAI-OpenSourcePapers — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo JunMa11/MICCAI-OpenSourcePapers solve, and who is the primary audience?
    pass
    AI did not name JunMa11/MICCAI-OpenSourcePapers — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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JunMa11/MICCAI-OpenSourcePapers — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite