QuPaS：量子力場ファインチューニングと敵対的推定によるSAMベースの半教師あり組織病理画像セグメンテーション

Semi-supervised segmentation (S³) is one of the preferred choices for histopathological image segmentation tasks, while how to improve model's learning capability for unlabeled data remains a key challenge in S³. The remarkable feature extraction abilities of Segment Anything Model (SAM) offers a potential opportunity. However, SAM's performance on contextual complex histopathological images is not so desirable due to its limitations in finely capture structural relationships. To address this issue, we propose a novel SAM-based S³ framework QuPaS, which consists of Quantum Force Field (QFF) Finetuning and Adversarial Estimation (AE). QFF covers the shortage of SAM's limited understanding of spatial structure by simulating intermolecular forces to explore the structural topological relationships between pixel-level features. AE introduces an adversarial estimation network to align the consistency of confidence distributions between different outputs, thereby reducing the interference of incompatible semantic features on the model. Extensive experiments across three challenging histopathological segmentation scenarios have demonstrate that our QuPaS completely outperforms the state-of-the-art S³ methods. Furthermore, QuPaS is able to maintain stable generalization performance on previously unseen domains. The code will be released at: https://github.com/director87/QuPaS.