发表机构
Institute for AI in Medicine (IKIM), University Hospital Essen; Institute for Anthropomatics and Robotics (IAR), Karlsruhe Institute of Technology（埃森大学医院人工智能医学研究所; 卡尔斯鲁厄理工学院人机交互与机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出双向连通分量损失（BiCC），从预测中派生实例以直接惩罚假阳性分量，平衡参数控制精确率-召回率权衡，在多个数据集上优于现有损失。
AI 中文摘要
常见的分割损失函数按体素聚合误差，因此病灶对目标函数的影响与其体积成正比，使得体积小但临床关键的病灶权重过低。实例感知损失旨在通过为每个病灶分配独立的损失项来解决这一不匹配问题。然而，blob损失和CC-DiceCE仅从标注中获取区域，因此假阳性分量得不到实例级别的惩罚。这在计算机辅助审查中尤为重要，因为每个假阳性分量可能需要单独检查，使得精确率和假阳性负担与召回率同等重要。我们提出了双向连通分量损失（BiCC），它配对标注和预测派生的分区，以在自身尺度上对预测分量进行评分。通过从预测中派生实例，该分支直接惩罚假阳性分量，无论其大小如何。平衡参数α允许控制病灶级别的精确率-召回率权衡。在五个数据集上使用nnU-Net进行五折交叉验证，BiCC在四个数据集上的病灶级F1优于CC-DiceCE，在所有五个数据集上优于blob损失。它在三个数据集上显著优于DiceCE，并在两个数据集上与之持平；而CC-DiceCE因偏好召回率而损失高达0.363的精确率。代码可在https URL获取。
英文摘要
Common segmentation losses aggregate errors voxel-wise, so lesions influence the objective in proportion to their volume, giving small but clinically critical lesions disproportionately little weight. Instance-aware losses aim to address this mismatch by assigning each lesion its own term. However, blob loss and CC-DiceCE derive their regions solely from annotations, so false-positive components receive no instance-level term. This matters in computer-assisted review, where each false-positive component may require separate inspection, making precision and false-positive burden important alongside recall. We introduce the bidirectional connected-component loss (BiCC), which pairs annotation- and prediction-derived partitions to score predicted components on their own scale. By deriving instances from the predictions, this branch directly penalizes false-positive components regardless of their size. The balance parameter $α$ allows control over the lesion-wise precision-recall trade-off. Across five datasets with five-fold cross-validation using nnU-Net, BiCC outperforms CC-DiceCE in lesion-wise F1 on four datasets and blob loss on all five. It significantly improves over DiceCE on three datasets and matches it on two; CC-DiceCE instead loses up to 0.363 precision by favoring recall. Code is available at https://github.com/TIO-IKIM/BiCC-Loss.
Comments2 figures, 3 tables. Code: https://github.com/TIO-IKIM/BiCC-Loss
