Dermoscopic-Image-ICL-GPT4
importedsoftware/dermoscopic-image-icl-gpt4
Dermoscopic Image In-Context Learning (ICL) with GPT4v
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Documentation
- unknown
- Tags
- dermatology · dermoscopic-image · gpt-4 · in-context-learning · llms · medical-image-analysis · pathology · prompt-engineering
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- DermaSwarmdermatology · llms
DermaSwarm is a production-grade multi-agent system designed for dermatologists to collaboratively diagnose and treat skin conditions. Leveraging the power of AI-driven agents, DermaSwarm…
- CASSIAprompt-engineering
CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework
- paper-image-pptprompt-engineering
中英双语医学科研配图与提示词规划 | Bilingual Codex skill for scientific medical illustration planning and prompt generation.
- gan-skin-matlabdermatology · medical-image-analysis
Example of how to use MATLAB to generate synthetic images of skin lesions.
- OpenGPTgpt-4
A framework for creating grounded instruction based datasets and training conversational domain expert Large Language Models (LLMs).
- medalignllms
MedAlign is a clinician-generated dataset for instruction following with electronic medical records.
- api.github.com/repos/JingeW/Dermoscopic-Image-ICL-GPT4retrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2024-09-13, 4 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/8.json→ .entries["dermoscopic-image-icl-gpt4"]
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