Title: PathChat-SegR1: Reasoning Segmentation in Pathology via SO-GRPO
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Clinical Reasoning, Reinforcement Learning，Reasoning Segmentation

Abstract: Pathological image segmentation faces critical challenges in generalizing to rare diseases and staining variations, while clinicians require interpretable outputs for diagnostic confidence. We present PathChat-SegR1, a reasoning-guided segmentation framework that addresses these challenges through three key contributions. First, we construct the PathChat-SegR1 Benchmark comprising 118,667 pathology image-mask-reasoning triplets, including challenging intraoperative frozen sections, establishing the first large-scale dataset for reasoning-driven pathological segmentation. Second, we develop a comprehensive framework that incorporates pathology-specific prior knowledge through RuiPath visual encoder integration, stain-invariant MedSAM pretraining, and parameter-efficient fine-tuning strategies. Third, and most critically, we propose SO-GRPO (Segmentation-Optimized GRPO), a novel reinforcement learning method that transforms the \texttt{<SEG>} token generation paradigm from fixed insertion to autonomous generation. SO-GRPO introduces four theoretical innovations: (1) temporal credit assignment via GAE for precise action-level optimization, (2) differentiable reward design enabling end-to-end gradient propagation, (3) information-theoretic sparsity control to balance generation necessity, and (4) convergence guarantees through Robbins-Monro conditions. These innovations collectively address key challenges in reinforcement learning for reasoning segmentation. Training on our benchmark, PathChat-SegR1 achieves 0.58 Dice on zero-shot pulmonary metastasis segmentation, significantly outperforming existing reasoning-based models. Remarkably, the model demonstrates training-free, one-shot adaptation capabilities, improving performance from 0.53 to 0.72 Dice on rare diseases with a single example at test time. This establishes a new paradigm for pathological image analysis where reasoning drives both segmentation accuracy and clinical interpretability. Our code and benchmark will be released at https://anonymous.4open.science/r/PathChat-Seg-3116.