Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.13626 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912000945160192 |
|---|---|
| author | Latorre, Laura Petrychenko, Liliana Beets-Tan, Regina Kopytova, Taisiya Silva, Wilson |
| author_facet | Latorre, Laura Petrychenko, Liliana Beets-Tan, Regina Kopytova, Taisiya Silva, Wilson |
| contents | We explore deep generative models to generate case-based explanations in a medical federated learning setting. Explaining AI model decisions through case-based interpretability is paramount to increasing trust and allowing widespread adoption of AI in clinical practice. However, medical AI training paradigms are shifting towards federated learning settings in order to comply with data protection regulations. In a federated scenario, past data is inaccessible to the current user. Thus, we use a deep generative model to generate synthetic examples that protect privacy and explain decisions. Our proof-of-concept focuses on pleural effusion diagnosis and uses publicly available Chest X-ray data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13626 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Case-based Interpretability for Medical Federated Learning Latorre, Laura Petrychenko, Liliana Beets-Tan, Regina Kopytova, Taisiya Silva, Wilson Machine Learning Artificial Intelligence We explore deep generative models to generate case-based explanations in a medical federated learning setting. Explaining AI model decisions through case-based interpretability is paramount to increasing trust and allowing widespread adoption of AI in clinical practice. However, medical AI training paradigms are shifting towards federated learning settings in order to comply with data protection regulations. In a federated scenario, past data is inaccessible to the current user. Thus, we use a deep generative model to generate synthetic examples that protect privacy and explain decisions. Our proof-of-concept focuses on pleural effusion diagnosis and uses publicly available Chest X-ray data. |
| title | Towards Case-based Interpretability for Medical Federated Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.13626 |