BackSplit：细分背景在生物医学病灶分割中的重要性
BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation
将二分类分割中的背景细分为多个解剖结构类别，利用现有分割模型自动生成辅助标签，在不增加推理成本的情况下提升小病灶分割性能，可推广到其他密集预测任务。
原文依据 training with fine-grained labels that sub-divide the background class, which we call BackSplit, is a simple yet powerful paradigm that can offer a significant performance boost without increasing inference costs.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在医学图像中分割小病灶仍然非常困难。以往大多数工作通过设计更好的架构、损失函数或数据增强方案，以及收集更多标注数据来应对这一挑战。我们采取不同的视角，认为部分问题在于背景的建模方式。常见的病灶分割将所有非病灶像素归为单一的“背景”类，忽略了病灶出现时丰富的解剖上下文。实际上，背景是高度异质的——由组织、器官和其他结构组成，这些结构现在可以手动标注或使用现有分割模型自动推断。在本文中，我们主张使用细分背景类的细粒度标签进行训练，我们称之为BackSplit，这是一种简单而强大的范式，可以在不增加推理成本的情况下显著提升性能。从信息论的角度，我们证明BackSplit相对于传统二分类训练增加了期望Fisher信息，从而带来更紧的渐近界和更稳定的优化。通过在多个数据集和架构上的大量实验，我们经验性地表明BackSplit持续提升小病灶分割性能，即使辅助标签是使用预训练分割模型自动生成的。此外，我们证明来自交互式分割框架的辅助标签也表现出相同的有益效果，展示了其鲁棒性、简单性和广泛适用性。
英文原文摘要 1604 字符
Segmenting small lesions in medical images remains notoriously difficult. Most prior work tackles this challenge by either designing better architectures, loss functions, or data augmentation schemes; and collecting more labeled data. We take a different view, arguing that part of the problem lies in how the background is modeled. Common lesion segmentation collapses all non-lesion pixels into a single "background" class, ignoring the rich anatomical context in which lesions appear. In reality, the background is highly heterogeneous--composed of tissues, organs, and other structures that can now be labeled manually or inferred automatically using existing segmentation models.In this paper, we argue that training with fine-grained labels that sub-divide the background class, which we call BackSplit, is a simple yet powerful paradigm that can offer a significant performance boost without increasing inference costs. From an information theoretic standpoint, we prove that BackSplit increases the expected Fisher Information relative to conventional binary training, leading to tighter asymptotic bounds and more stable optimization. With extensive experiments across multiple datasets and architectures, we empirically show that BackSplit consistently boosts small-lesion segmentation performance, even when auxiliary labels are generated automatically using pretrained segmentation models. Additionally, we demonstrate that auxiliary labels derived from interactive segmentation frameworks exhibit the same beneficial effect, demonstrating its robustness, simplicity, and broad applicability.