Have you ever wished to automate the tedious task of generating radiology reports? Meet RaDialog, a cutting-edge tool that leverages the power of AI to create medical reports from radiographic images seamlessly. This guide will take you through the process of getting started with RaDialog, including installation steps, running inference, and tips for troubleshooting.

Getting Started

First, ensure you have the right environment set up. Here’s how you can clone the repository and install all necessary requirements:

1. Clone the Repository

git clone https://huggingface.co/ChantalPellegrini/RaDialog-interactive-radiology-report-generation

2. Install Requirements

After cloning the repository, create a new conda environment and install the requirements:

conda create -n llava_hf python=3.10
conda activate llava_hf
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt

3. Run RaDialog Inference

Now, you’re ready to run the key inference code! The RaDialog utilizes a series of functions to analyze an image and generate a coherent report. Let’s break down the code into a simple analogy for better understanding:

Imagine you are an executive chef, and your kitchen is an elaborate restaurant. The ingredients you use (the input image) come from delivery boxes, each properly labeled (the model and tokenizer). Your sous-chefs (various functions and models) take those ingredients and start preparing the food (analyzing the image). They create a beautiful dish (the generated report) that is served to the customer (the user) who can enjoy the meal (the report). Each step is crucial for ensuring that the final dish is exquisite and meets the customer’s expectations. In the same way, RaDialog processes the image step by step to generate accurate and precise radiology reports.

# Import necessary libraries
from pathlib import Path
import io
import requests
import torch
from PIL import Image
import numpy as np
from huggingface_hub import snapshot_download
from LLAVA_Biovil.llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria, remap_to_uint8
from LLAVA_Biovil.llava.model.builder import load_pretrained_model
from LLAVA_Biovil.llava.conversation import SeparatorStyle, conv_vicuna_v1
from LLAVA_Biovil.llava.constants import IMAGE_TOKEN_INDEX
from utils import create_chest_xray_transform_for_inference, init_chexpert_predictor

# Example function to load the model from Hugging Face
def load_model_from_huggingface(repo_id):
    model_path = snapshot_download(repo_id=repo_id, revision='main', force_download=True)
    model_path = Path(model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, model_base='liuhaotian/llava-v1.5-7b', 
    model_name='llava-v1.5-7b-task-lora_radialog_instruct_llava_biovil_unfrozen_2e-5_5epochs_v5_checkpoint-21000', load_8bit=False, load_4bit=False)
    return tokenizer, model, image_processor, context_len

Troubleshooting

If you encounter any issues while running RaDialog, consider the following troubleshooting tips:

• Ensure that your environment has all the necessary libraries installed.
• Verify the compatibility of the Python and library versions you’re using.
• If specific image URLs are not accessible, test with a different image link or ensure that the desired image is publicly available.
• Look for any spelling or syntax errors in your code.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Give RaDialog a try and become a part of the revolution in radiology report generation!