标题： ScSAM：消除亚细胞语义分割中的形态偏差和分布变异性 显示英文标题

标题： ScSAM: Debiasing Morphology and Distributional Variability in Subcellular Semantic Segmentation

摘要： 亚细胞组分之间显著的形态和分布可变性给基于学习的细胞器分割模型带来了长期挑战，显著增加了偏差特征学习的风险。 现有方法通常依赖单一映射关系，忽视了特征多样性，从而导致偏差训练。 尽管Segment Anything Model（SAM）提供了丰富的特征表示，但其在亚细胞场景中的应用受到两个关键挑战的阻碍：(1) 亚细胞形态和分布的可变性在标签空间中造成了差距，导致模型学习到虚假或有偏差的特征。 (2) SAM专注于全局上下文理解，常常忽略细粒度的空间细节，使得捕捉细微结构变化和处理偏态数据分布变得困难。 为了解决这些挑战，我们引入了ScSAM，这是一种通过融合预训练SAM与掩码自编码器（MAE）引导的细胞先验知识来增强特征鲁棒性的方法，以缓解数据不平衡带来的训练偏差。 具体而言，我们设计了一个特征对齐和融合模块，将预训练嵌入对齐到相同的特征空间，并高效地结合不同的表示。 此外，我们提出了一种基于余弦相似度矩阵的类别提示编码器，以激活特定类别的特征来识别亚细胞类别。 在多种亚细胞图像数据集上的大量实验表明，ScSAM优于最先进的方法。 显示英文摘要

摘要： The significant morphological and distributional variability among subcellular components poses a long-standing challenge for learning-based organelle segmentation models, significantly increasing the risk of biased feature learning. Existing methods often rely on single mapping relationships, overlooking feature diversity and thereby inducing biased training. Although the Segment Anything Model (SAM) provides rich feature representations, its application to subcellular scenarios is hindered by two key challenges: (1) The variability in subcellular morphology and distribution creates gaps in the label space, leading the model to learn spurious or biased features. (2) SAM focuses on global contextual understanding and often ignores fine-grained spatial details, making it challenging to capture subtle structural alterations and cope with skewed data distributions. To address these challenges, we introduce ScSAM, a method that enhances feature robustness by fusing pre-trained SAM with Masked Autoencoder (MAE)-guided cellular prior knowledge to alleviate training bias from data imbalance. Specifically, we design a feature alignment and fusion module to align pre-trained embeddings to the same feature space and efficiently combine different representations. Moreover, we present a cosine similarity matrix-based class prompt encoder to activate class-specific features to recognize subcellular categories. Extensive experiments on diverse subcellular image datasets demonstrate that ScSAM outperforms state-of-the-art methods.