提升超声图像分割的可靠性与可解释性，实现快速精准诊断。
E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation
- 用动态概率权重重解SAM输出，免训练实现不确定性估计。
- 推理速度达0.03秒/图，平均DSC达89.0%，优于基线模型。
- 揭示关键决策令牌，适合临床高安全场景使用。
尽管分段任意模型（SAM）推动了医学图像分割的发展，但其在不确定性感知分割中的贝叶斯适配仍面临三大挑战：(1) 大型预训练SAM的贝叶斯微调不稳定；(2) 由于参数量巨大导致计算成本高；(3) 黑箱设计限制可解释性。为此，我们提出E-BayesSAM，融合逐标记变分贝叶斯推断（T-VBI）以高效实现贝叶斯适配，以及自优化科尔莫戈罗夫-阿诺德网络（SO-KAN）以增强可解释性。T-VBI将SAM输出标记重新解释为动态概率权重，并将其重参数化为潜在变量，无需辅助训练即可实现无训练变分贝叶斯推断以进行不确定性估计。SO-KAN通过自监督学习引入可学习样条激活函数，改进标记预测，揭示冗余标记并予以剪枝，从而提升效率与精度。在五个超声数据集上的实验表明，E-BayesSAM实现了：(i) 实时推理（0.03秒/图像），(ii) 更优的分割准确率（平均DSC：剪枝后的E-BayesSAM为89.0%，原版E-BayesSAM为88.0%，MedSAM为88.3%），(iii) 识别出四个决定SAM决策的关键标记。通过统一效率、可靠性与可解释性，E-BayesSAM弥合了SAM的通用性与临床需求之间的差距，推动其在高安全性医疗应用中的部署。源代码见https://github.com/mp31192/E-BayesSAM。
原文摘要 · Abstract (English)
Although the Segment Anything Model (SAM) has advanced medical image segmentation, its Bayesian adaptation for uncertainty-aware segmentation remains hindered by three key issues: (1) instability in Bayesian fine-tuning of large pre-trained SAMs; (2) high computation cost due to SAM's massive parameters; (3) SAM's black-box design limits interpretability. To overcome these, we propose E-BayesSAM, an efficient framework combining Token-wise Variational Bayesian Inference (T-VBI) for efficienty Bayesian adaptation and Self-Optimizing Kolmogorov-Arnold Network (SO-KAN) for improving interpretability. T-VBI innovatively reinterprets SAM's output tokens as dynamic probabilistic weights and reparameterizes them as latent variables without auxiliary training, enabling training-free VBI for uncertainty estimation. SO-KAN improves token prediction with learnable spline activations via self-supervised learning, providing insight to prune redundant tokens to boost efficiency and accuracy. Experiments on five ultrasound datasets demonstrated that E-BayesSAM achieves: (i) real-time inference (0.03s/image), (ii) superior segmentation accuracy (average DSC: Pruned E-BayesSAM's 89.0\% vs. E-BayesSAM's 88.0% vs. MedSAM's 88.3%), and (iii) identification of four critical tokens governing SAM's decisions. By unifying efficiency, reliability, and interpretability, E-BayesSAM bridges SAM's versatility with clinical needs, advancing deployment in safety-critical medical applications. The source code is available at https://github.com/mp31192/E-BayesSAM.
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