Self-supervised Pretraining on OAI data for 3D Knee MRI Analysis

Motivation: Self-supervised pretraining is efficient and requires no labeled data, yet it is understudied for 3D knee MRI analysis. Goal(s): Our goal is to develop a self-supervised pretraining model and explore its potential for 3D knee MRI analysis. Approach: We use DINO pipeline for self-supervised training on OAI data and apply the pretrained model to downstream tasks, comparing it with training from scratch and a supervised pretrained model. Results: Our OAI-DINO pretrained model significantly outperforms training from scratch on downstream tasks, offers comparable segmentation results and improves classification performance over the supervised pretrained model. Impact: Our study has leveraged the OAI database and demonstrated the effectiveness of self-supervised pretraining for 3D knee MRI. Our approach enhances downstream task performance, inspiring further study on advancing automated 3D medical imaging analysis without labeled data.