Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916384793952256 |
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| author | da Silva, Davide Clode Bernardes, Marina Musse Ceretta, Nathalia Giacomini de Souza, Gabriel Vaz Silva, Gabriel Fonseca Bordini, Rafael Heitor Musse, Soraia Raupp |
| author_facet | da Silva, Davide Clode Bernardes, Marina Musse Ceretta, Nathalia Giacomini de Souza, Gabriel Vaz Silva, Gabriel Fonseca Bordini, Rafael Heitor Musse, Soraia Raupp |
| contents | Machine learning has significantly advanced healthcare by aiding in disease prevention and treatment identification. However, accessing patient data can be challenging due to privacy concerns and strict regulations. Generating synthetic, realistic data offers a potential solution for overcoming these limitations, and recent studies suggest that fine-tuning foundation models can produce such data effectively. In this study, we explore the potential of foundation models for generating realistic medical images, particularly chest x-rays, and assess how their performance improves with fine-tuning. We propose using a Latent Diffusion Model, starting with a pre-trained foundation model and refining it through various configurations. Additionally, we performed experiments with input from a medical professional to assess the realism of the images produced by each trained model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04424 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques da Silva, Davide Clode Bernardes, Marina Musse Ceretta, Nathalia Giacomini de Souza, Gabriel Vaz Silva, Gabriel Fonseca Bordini, Rafael Heitor Musse, Soraia Raupp Image and Video Processing Computer Vision and Pattern Recognition Graphics Machine learning has significantly advanced healthcare by aiding in disease prevention and treatment identification. However, accessing patient data can be challenging due to privacy concerns and strict regulations. Generating synthetic, realistic data offers a potential solution for overcoming these limitations, and recent studies suggest that fine-tuning foundation models can produce such data effectively. In this study, we explore the potential of foundation models for generating realistic medical images, particularly chest x-rays, and assess how their performance improves with fine-tuning. We propose using a Latent Diffusion Model, starting with a pre-trained foundation model and refining it through various configurations. Additionally, we performed experiments with input from a medical professional to assess the realism of the images produced by each trained model. |
| title | Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2409.04424 |