Unified Medical Image Tokenizer for Autoregressive Synthesis and Understanding

ArXi:2505.19225v2 Announce Type: replace-cross Autoregressive modeling has driven major advances in multimodal AI, yet its application to medical imaging remains constrained by the absence of a unified image tokenizer that simultaneously preserves fine-grained anatomical structures and rich clinical semantics across heterogeneous modalities. Existing approaches jointly optimize image reconstruction and textual semantic objectives, relying on large-scale image-caption pairs and are prone to gradient interference.