REPOGEO REPORT · LITE

YichiZhang98/SAM4MIS

Default branch main · commit 745e76e4 · scanned 5/22/2026, 5:03:03 PM

GitHub: 1,103 stars · 77 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

23 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 fail

Objective metadata checks

AI knows your name

2 / 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 YichiZhang98/SAM4MIS, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

medical-image-segmentation, segment-anything-model, foundation-models, medical-ai, computer-vision, survey, research-tracker

highreadme#2

Reposition README H1 and opening sentence to emphasize survey/tracker role

Why:

CURRENT

# Segment Anything Model / Foundation Models for Medical Image Segmentation (SAM4MIS)

*  Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision.

COPY-PASTE FIX

Change H1 to: # SAM4MIS: A Comprehensive Research Tracker and Survey of Segment Anything Model & Foundation Models for Medical Image Segmentation. Add or rephrase the first sentence of the README to: "This repository serves as a comprehensive, continuously updated tracker and survey of the latest research progress and applications of the Segment Anything Model (SAM) and other Foundation Models in medical image segmentation."

highlicense#3

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file in the repository root. Choose an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and add its full text to this file.

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 YichiZhang98/SAM4MIS

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Med-PaLM M

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Med-PaLM M · recommended 2×
Swin Transformer · recommended 1×
Vision Transformer (ViT) · recommended 1×
Masked Autoencoders (MAE) · recommended 1×
TransUNet · recommended 1×

CATEGORY QUERY
What are effective methods for applying large vision models to medical image segmentation?
you: not recommended
AI recommended (in order):
1. Swin Transformer
2. Vision Transformer (ViT)
3. Masked Autoencoders (MAE)
4. TransUNet
5. UNETR (UNET Transformer)
6. nnUNet
7. DINO (Self-Distillation with No Labels)
8. BYOL (Bootstrap Your Own Latent)
9. MONAI (Medical Open Network for AI)
10. Med-PaLM M
11. Segment Anything Model (SAM)
12. Pathology Foundation Models
AI recommended 12 alternatives but never named YichiZhang98/SAM4MIS. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find a survey of foundation models for medical image segmentation research?
you: not recommended
AI recommended (in order):
1. Med-PaLM M
2. SAM (Segment Anything Model)
3. UNETR
AI recommended 3 alternatives but never named YichiZhang98/SAM4MIS. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
fail
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 YichiZhang98/SAM4MIS?
pass
AI named YichiZhang98/SAM4MIS explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts YichiZhang98/SAM4MIS in production, what risks or prerequisites should they evaluate first?
pass
AI named YichiZhang98/SAM4MIS explicitly
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 YichiZhang98/SAM4MIS solve, and who is the primary audience?
pass
AI did not name YichiZhang98/SAM4MIS — 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?

Embed your GEO score

Drop this badge into the README of YichiZhang98/SAM4MIS. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/YichiZhang98/SAM4MIS.svg)](https://repogeo.com/en/r/YichiZhang98/SAM4MIS)

HTML

<a href="https://repogeo.com/en/r/YichiZhang98/SAM4MIS"><img src="https://repogeo.com/badge/YichiZhang98/SAM4MIS.svg" alt="RepoGEO" /></a>

Pro

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YichiZhang98/SAM4MIS — 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