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Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challenges including limited data availability, lack of generalizability, and the necessity to incorporate multi-modal data effectively. A foundation model, which is a large-scale pre-trained AI model, offers a versatile base that can be adapted to a variety of specific tasks and contexts. Here, we present a novel foundation model, VISION-MAE, specifically designed for medical imaging. Specifically, VISION-MAE is trained on a dataset of 2.5 million unlabeled images from various modalities (CT, MR, PET, X-rays, and ultrasound), using self-supervised learning techniques. It is then adapted to classification and segmentation tasks using explicit labels. VISION-MAE has high label efficiency, outperforming several benchmark models in both in-domain and out-of-domain applications, and achieves high performance even with reduced availability of labeled data. This model represents a significant advancement in medical imaging AI, offering a generalizable and robust solution for improving segmentation and classification tasks while reducing the data annotation workload.

Purpose To develop a deep learning model for multi-anatomy and many-class segmentation of diverse anatomic structures on MRI imaging. Materials and Methods In this retrospective study, two datasets were curated and annotated for model development and evaluation. An internal dataset of 1022 MRI sequences from various clinical sites within a health system and an external dataset of 264 MRI sequences from an independent imaging center were collected. In both datasets, 49 anatomic structures were annotated as the ground truth. The internal dataset was divided into training, validation, and test sets and used to train and evaluate an nnU-Net model. The external dataset was used to evaluate nnU-Net model generalizability and performance in all classes on independent imaging data. Dice scores were calculated to evaluate model segmentation performance. Results The model achieved an average Dice score of 0.801 on the internal test set, and an average score of 0.814 on the complete external dataset across 49 classes. Conclusion The developed model achieves robust and generalizable segmentation of 49 anatomic structures on MRI imaging. A future direction is focused on the incorporation of additional anatomic regions and structures into the datasets and model.

Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadImageNet is highly resource-intensive. To address these limitations, we introduce RadImageGAN, the first multi-modal radiologic data generator, which was developed by training StyleGAN-XL on the real RadImageNet dataset of 102,774 patients. RadImageGAN can generate high-resolution synthetic medical imaging datasets across 12 anatomical regions and 130 pathological classes in 3 modalities. Furthermore, we demonstrate that RadImageGAN generators can be utilized with BigDatasetGAN to generate multi-class pixel-wise annotated paired synthetic images and masks for diverse downstream segmentation tasks with minimal manual annotation. We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning. This shows that RadImageGAN combined with BigDatasetGAN can improve model performance and address data scarcity while reducing the resources needed for annotations for segmentation tasks.