Towards Generalizable Tumor Synthesis
This work addresses the need for scalable and generalizable tumor synthesis to improve AI-based tumor detection in medical imaging, representing an incremental advance in the field.
The paper tackled the problem of creating generalizable synthetic tumors for training AI models by leveraging the similarity of early-stage tumors across organs in CT scans, and demonstrated that models trained on these synthetic tumors can detect and segment real tumors across diverse patient demographics and imaging protocols.
Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation. However, success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and, furthermore, the resulting AI models being capable of detecting real tumors in images sourced from different domains (e.g., hospitals). This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation: early-stage tumors (< 2cm) tend to have similar imaging characteristics in computed tomography (CT), whether they originate in the liver, pancreas, or kidneys. We have ascertained that generative AI models, e.g., Diffusion Models, can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ. Moreover, we have shown that AI models trained on these synthetic tumors can be generalized to detect and segment real tumors from CT volumes, encompassing a broad spectrum of patient demographics, imaging protocols, and healthcare facilities.