Generating Synthetic Medical Images Using Fine-Tuned Models

Machine learning has become important in healthcare, helping with the prevention of diseases and finding treatments. However, getting access to patient data is hard because of privacy rules and strict laws. One way to deal with this is by creating synthetic data, which means generating fake but realistic data that can be used for research. New studies show that Fine-tuningFoundation Models can help create this synthetic data effectively.

This article looks into using foundation models to generate realistic medical images, especially Chest X-rays. We will see how fine-tuning these models can improve their performance. Our approach involves using a Latent Diffusion Model, which starts with a basic pre-trained model and refines it through different configurations. We also worked with a medical professional to evaluate how realistic the images produced by the models are.

#The Importance of Machine Learning in Healthcare

Recently, machine learning has played an important part in healthcare. For example, it can analyze big sets of data to find patterns and predict how diseases will progress. This ability is crucial for understanding and treating serious conditions, such as cancer.

Despite its benefits, the use of machine learning in healthcare has not been widespread. Some reasons include limited patient data, privacy issues, and the strict regulations that healthcare decisions must follow. Protecting patient information from unauthorized access is crucial, making it difficult to gather the data needed for machine learning.

#Creating Synthetic Medical Data

Generating high-quality synthetic medical data could help address some of these challenges. The healthcare industry expects that the availability of synthetic data will increase significantly in the coming years, making it a potential alternative to real patient data.

One area where synthetic data can be useful is in creating medical images. Generative models can create realistic images from text descriptions, and some studies focus on fine-tuning foundation models with small datasets to achieve better results. Foundation models are machine learning models trained on a wide range of general data, often using self-supervision, which refers to the model learning from the data itself without needing a labeled dataset.

Some examples of foundation models include notable names like ELMo, GPT-3, CLIP, ResNet, DALL-E, and Stable Diffusion. These models have made great strides in various complex tasks, such as answering questions and retrieving information. Fine-tuning these models helps adapt their general capabilities for specific applications like generating medical images.

#Related Work

Many studies have looked into techniques for generating images from textual descriptions. In one study, a model called Re-Imagen was created to generate accurate images by utilizing retrieved information. This model can create realistic images, even for rare or unknown entities.

Another study introduced LAFITE, which allows training for text-to-image models without the need for many image-text pairs. This can help in reducing the challenges posed by having to gather large datasets.

In the medical realm, models have been developed to create synthetic images from high-resolution brain MRIs. These models learn how brain images look based on factors such as age and sex. They use a combination of autoencoders and diffusion models to generate new images from learned data.

Some researchers have also looked directly at synthesizing medical images. For example, one study used a pre-trained model to generate lung X-ray and CT images, while another used a large chest X-ray dataset for generating realistic images. The goal of these studies is to create high-quality medical images while respecting privacy concerns.

#Proposed Method

In our study, we focus on fine-tuning a Latent Diffusion Model to generate high-resolution synthetic chest X-ray images. We use a publicly available dataset that contains both healthy and unhealthy cases related to tuberculosis. The dataset has a total of 138 chest X-ray images, with 80 being normal and the rest showing tuberculosis.

From this dataset, we used a smaller set of 30 images for our initial testing, with half being healthy and half being unhealthy. This limited size helped us better understand the model's capability and guided future research steps.

We used a user-friendly interface, called Kohya-ss GUI, to set up and fine-tune our diffusion models. This interface allows us to choose different fine-tuning techniques. We decided to use Low-Rank Adaptation (LoRA) because it requires fewer resources and is easier to adapt for our needs.

In our fine-tuning process, we used different optimizers to see how they affected image generation. Some of the optimizers included AdamW8bit, Adafactor, DAdaptSGD, and Prodigy. Each optimizer has its unique features that help adjust how the model learns from the data.

#Experimental Results

We generated images of chest X-rays using six different models: one pre-trained foundational model and five models fine-tuned with different optimizers. Each model produced sets of images based on prompts that described normal and abnormal cases.

A medical doctor evaluated the realism of the generated images on a scale from 1 (Very Unrealistic) to 5 (Very Realistic). The evaluation found that the foundational model produced images that were quite unrealistic. However, two fine-tuned models performed better, with one achieving a score of 5 for normal cases. This indicates that fine-tuning can lead to more realistic image generation, even with a smaller dataset.

#Discussion and Limitations

Our study shows that fine-tuning foundation models can lead to improved image realism in generating medical images, specifically chest X-rays. The experiments demonstrated that using a small dataset could still yield satisfactory results.

However, we acknowledge the limitations of our study. The evaluation relied on feedback from just one medical professional, and the assessment was based solely on visual inspection. More varied validation methods could provide a better overall picture of the model's performance.

Future work could involve testing different dataset sizes and training times, while also seeking input from a wider group of medical professionals. The optimizer that showed the best results, Adam8bit, could be explored more in upcoming experiments. We also aim to test different prompts for generating abnormal images since there are numerous conditions that may need representation.

#Conclusion

The findings from this work emphasize the potential of using fine-tuned foundation models for generating synthetic medical images. This approach can help overcome some challenges in accessing real patient data, all while producing images that can be used for healthcare and educational purposes.

We envision developing applications that allow educators or researchers to utilize this method in creating tailored examples for their needs. Such advancements could enhance the learning experience, making it more interactive and informative for students and professionals alike.

In summary, generating synthetic medical data through foundation models could play a vital role in research and education, potentially leading to advancements in patient care and medical training.