PHP SDK for integrate with the Dermatology API
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Updated
Sep 20, 2024 - PHP
PHP SDK for integrate with the Dermatology API
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 cross-checks peer-reviewed dermatology research to ensure diagnosis accuracy, generates treatment plans, and outputs results in easy-to-use JSON format.
Dermoscopic Image In-Context Learning (ICL) with GPT4v
Official code for "DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images". A data generation pipeline for creating photorealistic in-the-wild synthetic dermatalogical data with rich multi-task annotations for various skin-analysis tasks.
[MICCAI ISIC 2024] Code for "Lesion Elevation Prediction from Skin Images Improves Diagnosis"
Data and code for our analysis of DermaMNIST (MedMNIST), HAM10000, and Fitzpatrick17k datasets
LesNet (Lesion Net) is an open-source project for AI-based skin lesion detection. It aims to create a reliable tool and foster community involvement in critical AI problems. Contributions are welcome!
Clustering images of skin diseases using DINOv2 embeddings and dimensionality reduction techniques.
Transparent medical image AI via an image–text foundation model grounded in medical literature
Symfony bundle for integrate with the Dermatology API
Melanoma Deep Learning Project: Leveraging the power of deep learning for the detection and analysis of melanoma in medical images. This repository features Python and Jupyter Notebook resources aimed at advancing dermatological diagnostics through artificial intelligence
Dermatoscopic Image Classification: Exploring the classification of cancerous & pre-cancerous lesions.
his study analyzed 10-year dermatomycosis visit frequency and associated sociodemographic features to assess disease pattern and trend.
My implementation of a Customer Relationship Managegement (CRM) system, based around a startup pharmaceutical company that centres around dermatology. The system involves CRM as well as graphical sales figures (graphs, top ten sellers/buyers) built from the client's input data.
Using a GAN to synthetically generate medical images for DL purposes
Scripts used in "Deep learning for decision support in dermatology". This work was presented as a M. Sc. Thesis for the Technical University of Denmark (DTU).
A local app that runs in your browser based on a deep learning system that can classify an image in 2 ways: Binary classification of skin vs. non-skin. 304 disease categories.
CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions. Mirror of https://github.com/arezou-pakzad/CIRCLe
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