| title | emoji | colorFrom | colorTo | sdk | app_file | pinned |
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Medical image retrieval using a CLIP model |
🩺 |
red |
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streamlit |
app.py |
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This repository contains the code for fine-tuning a CLIP model [Arxiv paper][OpenAI Github Repo] on the ROCO dataset, a dataset made of radiology images and a caption. This work is done as a part of the Flax/Jax community week organized by Hugging Face and Google.
SciBERT (allenai/scibert_scivocab_uncased on 🤗) is used as the casual language model.
[🤗 Model card] [Streamlit demo]
You can try a Streamlit demo app that uses this model on 🤗 Spaces. You may have to signup for 🤗 Spaces private beta to access this app (screenshot shown below).
The demo can be run locally in the browser with
streamlit run /home/kaushalya/coding/medclip/app.py
Each image is accompanied by a textual caption. The caption length varies from a few characters (a single word) to 2,000 characters (multiple sentences). During preprocessing we remove all images that has a caption shorter than 10 characters. Training set: 57,780 images with their caption. Validation set: 7,200 Test set: 7,650
[ ] Give an example
This repo depends on the master branch of Hugging Face - Transformers library. First you need to clone the transformers repository and then install it locally (preferably inside a virtual environment) with pip install -e ".[flax]".
You can load the pretrained model from the Hugging Face Hub with
from medclip.modeling_hybrid_clip import FlaxHybridCLIP
model = FlaxHybridCLIP.from_pretrained("flax-community/medclip-roco")
Alternatively you can download the model checkpoint from [🤗 Model card].
The model is trained using Flax/JAX on a cloud TPU-v3-8.
You can fine-tune a CLIP model implemented in Flax by simply running sh run_medclip.
This is the validation loss curve we observed when we trained the model using the run_medclip.sh script.
The current model is capable of identifying higher level features such as the modality of ain image (e.g., if a given radiology image is a PET scan or an ultrasound scan). However it fails at identifying a brain scan from a lung scan. ❗️This model should not be used in a medical setting without further evaluations❗️.
Huge thanks to the Hugging Face 🤗 team and Google JAX/Flax team for organizing the community week and letting us use cloud compute for 2 weeks. We specially thank @patil-suraj & @patrickvonplaten for the continued support on Slack and the detailed feedback.
[ ] Mention more examples
[ ] Evaluation on down-stream tasks
[ ] Zero-shot learning performance
