用知识蒸馏提升癌组织细胞分割的鲁棒性，尤其在标注少时表现更优。
CellGenNet: A Knowledge-Distilled Framework for Robust Cell Segmentation in Cancer Tissues
- 教师-学生架构：教师用少量标注生成软标签，学生融合真标签与伪标签训练。
- 在多个癌症组织数据集上，分割精度和泛化能力优于监督与半监督基线。
- 适合标注稀缺的病理图像分析场景，助力可复现的癌症研究。
显微全片扫描图像（WSIs）中核分割因染色、成像条件和组织形态差异而困难。我们提出CellGenNet，一种在有限标注下实现跨组织细胞分割的鲁棒知识蒸馏框架。该框架采用学生-教师架构：容量较大的教师模型基于稀疏标注训练，并为未标注区域生成软伪标签；学生模型通过联合目标优化，结合真实标签、教师提供的概率目标及混合损失函数（二元交叉熵与Tversky损失），实现不对称惩罚以缓解类别不平衡并更好保留少数核结构。一致性正则化与逐层丢弃进一步稳定特征表示，促进可靠特征迁移。在多种癌症组织的全片扫描图像上实验表明，CellGenNet在分割精度与泛化能力上均优于监督与半监督基线，支持可扩展、可复现的组织病理学分析。
原文摘要 · Abstract (English)
Accurate nuclei segmentation in microscopy whole slide images (WSIs) remains challenging due to variability in staining, imaging conditions, and tissue morphology. We propose CellGenNet, a knowledge distillation framework for robust cross-tissue cell segmentation under limited supervision. CellGenNet adopts a student-teacher architecture, where a capacity teacher is trained on sparse annotations and generates soft pseudo-labels for unlabeled regions. The student is optimized using a joint objective that integrates ground-truth labels, teacher-derived probabilistic targets, and a hybrid loss function combining binary cross-entropy and Tversky loss, enabling asymmetric penalties to mitigate class imbalance and better preserve minority nuclear structures. Consistency regularization and layerwise dropout further stabilize feature representations and promote reliable feature transfer. Experiments across diverse cancer tissue WSIs show that CellGenNet improves segmentation accuracy and generalization over supervised and semi-supervised baselines, supporting scalable and reproducible histopathology analysis.
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