Abstract
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.
Abstract (translated)
标注医学图像需要大量的时间和专业知识，病理学家通常要花费数百小时来标记乳腺上皮细胞核数据集。为了解决这一关键挑战，我们使用仅599张训练图像就达到了95.5%的Dice分数，用于乳腺细胞分割任务，其中只有4%的像素代表了乳腺组织，并且有60%的图像是没有乳腺区域的。 我们的框架利用量子启发式的边缘增强技术，通过多尺度Gabor滤波器创建了一个第四输入通道，这有助于提高边界检测精度，在注释者之间差异可达±3个像素的情况下尤其有用。我们提出了一种稳定化的多重损失函数，该函数结合了自适应Dice损失和边界感知项以及自动正权重调整，以有效解决严重的类别不平衡问题，乳腺上皮细胞区域仅占总图像面积的0.1%-20%。 此外，我们引入了一种基于复杂度加权采样策略，优先处理具有挑战性的乳腺上皮细胞区域。该模型采用了EfficientNet-B7/UNet++架构，并采用4到3通道投影技术，这使得即使在医学影像数据有限的情况下也能使用预训练的权重。最后，通过指数移动平均和统计异常值检测实现稳健验证，在一个小规模验证集（129张图像）上提供可靠的性能估计。 我们的框架实现了95.5% ± 0.3%的Dice分数和91.2% ± 0.4%的交并比。值得一提的是，量子增强技术使边界准确性提高了2.1%，而加权采样则增加了小病变检测能力达3.8%。 通过在有限标注数据下实现突破性性能，我们的方法显著减少了医学专家为创建数据集所需的时间，从而解决了临床感知AI开发中的基本瓶颈问题。
URL
https://arxiv.org/abs/2512.02302
