针对小脑病变分割漏检问题，提出新型损失函数提升检测率。
Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation
- 基于病变实例设计新损失函数，关注单个病灶分割质量。
- 在保持分割精度前提下，显著提高小病灶检测召回率。
- 适用于医学图像中小病灶分割，尤其适合数据量有限场景。
传统医学图像分割中的损失函数（如Dice）因小病灶相对体积过小，导致其对整体损失贡献微乎其微，易造成漏分割。为此，研究者提出了基于实例的损失函数与评估指标。本文提出一种基于CC-Metrics框架的新型损失函数CC-DiceCE，与现有blob loss进行对比，并在nnU-Net框架下与DiceCE基线模型进行基准测试。实验表明，采用CC-DiceCE损失可显著提升小病灶检测召回率，同时几乎不降低分割性能，尽管在不同数据集上存在精度上的权衡。多数据集验证显示，CC-DiceCE总体优于blob loss。
原文摘要 · Abstract (English)
Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall loss. To address this, instance-wise loss functions and metrics have been proposed to evaluate segmentation quality on a per-lesion basis. We introduce CC-DiceCE, a loss function based on the CC-Metrics framework, and compare it with the existing blob loss. Both are benchmarked against a DiceCE baseline within the nnU-Net framework, which provides a robust and standardized setup. We find that CC-DiceCE loss increases detection (recall) with minimal to no degradation in segmentation performance, though with dataset-dependent trade-offs in precision. Furthermore, our multi-dataset study shows that CC-DiceCE generally outperforms blob loss.
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