Medical Imaging
ITRI: Finetuning and Enhancement using Latent Diffusion for medical image
Goal: Fine-tune a pretrained diffusion model on a new medical domain (different organ) and use the adapted model for image enhancement.
Motivation: Medical imaging data for certain organs is often limited. To address this, we propose leveraging the prior knowledge embedded in pretrained diffusion models from other organs. By fine-tuning on the target organ, the model can better generalize, enabling effective image enhancement and synthetic data generation in data-scarce settings.
Style transfer using transformer-based for ultrasound medical image
Goal: Improve ultrasound image translation to enhance downstream task performance across devices.
Motivation: Different ultrasound devices produce varying styles, which hinder model generalization. Existing UI2I methods overlook task-relevant style filtering, leading to suboptimal results. Our proposed approach addresses this by preserving content and selectively transferring useful style features.