ProFuseGPT: Progressive Fusion with Contrastive Refinement for Long-Sequence Medical Report Generation

Abstract

Automatic medical report generation (MRG) holds promise for alleviating radiologists’ workload, which has spurred growing interest in MRG for stroke diagnosis. However, existing approaches often fail to effectively model its long-range spatial dependencies and suppress phase-level noise in multi-slice sequences, leading to diluted pathological signals and unstable cross-modal alignment. To address this, we propose a novel framework integrating a progressive fusion mechanism (PFM) and intermediate state refinement via contrastive alignment (ISRCA), inspired by radiologists' clinical workflow. PFM progressively refines pathological representations through anatomically deviation-aware adaptive weighting, suppressing noise from normal slices while enhancing salient abnormalities from local deviations to global context integration. ISRCA adopts a teacher-student distillation approach using contrastive learning to mitigate noise propagation and stabilize intermediate report features. Experimental results on two stroke imaging datasets demonstrate that our method outperforms existing approaches in natural language generation (NLG) metrics, highlighting the effectiveness of PFM and ISRCA in handling long-sequence medical images and advancing stroke imaging report generation.