Matthew Leipzig

Joseph Cho, Mrudang Mathur, Cyril Zakka et al.

Deep learning algorithms require extensive data to achieve robust performance. However, data availability is often restricted in the medical domain due to patient privacy concerns. Synthetic data presents a possible solution to these challenges. Image generative models have found increasing use for medical applications, but are often task-specific, thus limiting their scalability. Moreover, existing models frequently rely on private datasets for training, which constrain their reproducibility. To address this, we introduce MediSyn: an open-access, generalist, text-guided latent diffusion model capable of generating synthetic images across 6 medical specialties and 10 imaging modalities, while being trained exclusively on publicly available data. Through extensive experimentation, we provide several key contributions. First, we demonstrate that training a generative model on visually diverse medical images does not degrade synthetic image quality. Second, we show that this generalist approach is substantially more computationally efficient than a coordinated suite of task-specific models. Third, we establish that a generalist model can produce realistic, text-aligned synthetic images across visually and medically distinct modalities, as validated by expert physicians. Fourth, we provide empirical evidence that these synthetic images are visually distinct from their corresponding real patient images, alleviating concerns about data memorization in image generative models. Finally, we demonstrate that a generalist model can produce synthetic images that improve classifier performance in data-limited settings across multiple medical specialties. Altogether, our findings highlight the immense potential of generalist image generative models to accelerate algorithmic research and development in medicine.

Rohan Shad, Cyril Zakka, Dhamanpreet Kaur et al.

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank and two additional publicly available external datasets. We explore emergent capabilities of our system and demonstrate remarkable performance across a range of tasks, including the problem of left-ventricular ejection fraction regression and the diagnosis of 39 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep-learning system is capable of not only contextualizing the staggering complexity of human cardiovascular disease but can be directed towards clinical problems of interest, yielding impressive, clinical-grade diagnostic accuracy with a fraction of the training data typically required for such tasks.

Joseph Cho, Mrudang Mathur, Cyril Zakka et al.

Deep learning algorithms require extensive data to achieve robust performance. However, data availability is often restricted in the medical domain due to patient privacy concerns. Synthetic data presents a possible solution to these challenges. Recently, image generative models have found increasing use for medical applications but are often designed for singular medical specialties and imaging modalities, thus limiting their broader utility. To address this, we introduce MediSyn: a text-guided, latent diffusion model capable of generating synthetic images from 6 medical specialties and 10 image types. Through extensive experimentation, we first demonstrate that MediSyn quantitatively matches or surpasses the performance of specialist models. Second, we show that our synthetic images are realistic and exhibit strong alignment with their corresponding text prompts, as validated by a team of expert physicians. Third, we provide empirical evidence that our synthetic images are visually distinct from their corresponding real patient images. Finally, we demonstrate that in data-limited settings, classifiers trained solely on synthetic data or real data supplemented with synthetic data can outperform those trained solely on real data. Our findings highlight the immense potential of generalist image generative models to accelerate algorithmic research and development in medicine.