Title: AttTok: Marrying Attribute Tokens with Generative Pre-trained Vision-Language Models towards Medical Image Understanding
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Medical generative pre-trained models, medical Multi-Modal alignment, medical VQA, instruction tuning

Abstract: Recent generative pre-trained vision–language (GPTv) models have achieved remarkable success in multi-modal understanding, inspiring their adaptation to medical imaging tasks such as disease diagnosis and visual question answering (VQA). However, current instruction-tuned GPTv models suffer from two key challenges: (1) medical attributes (e.g., disease names, severity grades) are encoded as plain text tokens, collapsing semantically distinct concepts into nearly identical textual sequences; and (2) inadequate textual supervision weakens visual representation learning, leading to severe inter-attribute confusion and misaligned vision–language embeddings. To address these limitations, we introduce attribute tokens (AttTok), a set of pre‑defined special tokens that uniquely encode clinical attributes (e.g., imaging modality, diagnosis, severity) within a structured token space. Complemented by attribute‑centric embedding books, AttTok serves as anchor points for aligning both visual and textual modalities into a shared, discriminative representation space. Building on this foundation, we design two key components: an attribute‑centric cross attention (ACC) adapter, which breaks the vision‑to‑text information‑flow bottleneck and enriches the visual encoder with discriminative attribute knowledge, and an attribute‑centric matching (ACM) loss, which enforces robust multi‑modal alignment centered on the attribute tokens. Extensive experiments on five medical classification benchmarks and three VQA datasets demonstrate that AttTok substantially improves both discriminative accuracy and medical knowledge reasoning, establishing a new paradigm for medical GPTv models with clinically discriminative understanding.