用极少标注数据实现高精度乳腺细胞分割，突破医疗影像标注瓶颈。
Breast Cell Segmentation Under Extreme Data Constraints: Quantum Enhancement Meets Adaptive Loss Stabilization
- 引入量子启发的多尺度Gabor边缘增强，提升边界检测精度。
- 在仅599张图、0.1%-20%像素为细胞区域下达95.5%的Dice分数。
- 适合标注资源稀缺但需高精度分割的医学图像研究者。
标注医学图像耗时耗力，常需病理科医生投入数百小时标注乳腺上皮核数据集。本文仅用599张训练图像即实现95.5%的Dice分数，其中仅4%像素为乳腺组织，60%图像不含乳腺区域。框架采用量子启发的多尺度Gabor滤波生成第四通道输入，增强边界检测，缓解标注者间差异（±3像素）。提出稳定化的多组分损失函数，结合自适应Dice损失、边界感知项与自动正样本加权，有效应对严重类别不平衡（细胞区域占图像面积0.1%-20%）。引入基于复杂度的加权采样策略，优先处理难检乳腺细胞区域。模型采用EfficientNet-B7/UNet++架构，通过4→3通道投影复用预训练权重。通过指数移动平均与统计异常值检测，在129张小规模验证集上实现可靠评估。最终模型取得95.5%±0.3% Dice和91.2%±0.4% IoU。量子增强使边界准确率提升2.1%，加权采样提高小病灶检出率3.8%。该方法显著降低专家标注需求，突破临床感知AI发展的核心瓶颈。
原文摘要 · Abstract (English)
Annotating medical images demands significant time and expertise, often requiring pathologists to invest hundreds of hours in labeling mammary epithelial nuclei datasets. We address this critical challenge by achieving 95.5% Dice score using just 599 training images for breast cell segmentation, where just 4% of pixels represent breast tissue and 60% of images contain no breast regions. Our framework uses quantum-inspired edge enhancement via multi-scale Gabor filters creating a fourth input channel, enhancing boundary detection where inter-annotator variations reach +/- 3 pixels. We present a stabilized multi-component loss function that integrates adaptive Dice loss with boundary-aware terms and automatic positive weighting to effectively address severe class imbalance, where mammary epithelial cell regions comprise only 0.1%-20% of the total image area. Additionally, a complexity-based weighted sampling strategy is introduced to prioritize the challenging mammary epithelial cell regions. The model employs an EfficientNet-B7/UNet++ architecture with a 4-to-3 channel projection, enabling the use of pretrained weights despite limited medical imaging data. Finally, robust validation is achieved through exponential moving averaging and statistical outlier detection, ensuring reliable performance estimates on a small validation set (129 images). Our framework achieves a Dice score of 95.5% +/- 0.3% and an IoU of 91.2% +/- 0.4%. Notably, quantum-based enhancement contributes to a 2.1% improvement in boundary accuracy, while weighted sampling increases small lesion detection by 3.8%. By achieving groundbreaking performance with limited annotations, our approach significantly reduces the medical expert time required for dataset creation, addressing a fundamental bottleneck in clinical perception AI development.
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