Deterministic Guided Progressive Medical Image Cross-Modal Generation Based on Deep Learning

Chujie Zhang, Lanfen Lin, Yen-Wei Chen; Proceedings of the Asian Conference on Computer Vision (ACCV) Workshops, 2024, pp. 394-404

In this paper, we address critical challenges in medical image generation using deep learning techniques. While convolutional neural networks and generative adversarial networks (GANs) have achieved remarkable results in various image generation tasks, their application to medical imaging faces unique obstacles. These include the complexity and diversity of medical images, limitations in discriminator network structures, and the risk of model collapse and gradient vanishing in multiscale discriminators. To overcome these issues, we propose a novel deterministic guided progressive GAN that specifically targets regions of interest (ROI) in medical images. Our approach progressively integrates adversarial generative networks, evolving from single-scale to multi-scale discriminators, to produce higher quality images. We demonstrate the efficacy of our model in generating high-precision cross-modal medical images through four comprehensive evaluation criteria, providing both quantitative and qualitative evidence of its performance compared to real images. This innovative method promises to significantly advance the field of medical image generation, potentially enhancing diagnostic accuracy and research capabilities in healthcare.