解决3D医学图像分割中主动学习评估的四大缺陷，提升标注效率。
nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation
- 构建基于3D块采样的主动学习框架，支持部分标注训练。
- 提出前景感知随机采样，缓解医学图像背景冗余问题。
- 引入前景效率指标，更真实反映标注成本，适合医学领域研究者。
语义分割在多种生物医学应用中至关重要，但其依赖大量标注数据，而人工标注成本高且需专业知识。主动学习（AL）通过仅查询最有信息量的样本以减少标注工作量。然而，在3D生物医学成像领域，尚无共识表明AL始终优于随机采样。现有评估存在四方面缺陷：（1）仅限少数数据集与标注预算；（2）在3D图像上使用2D模型且未采用部分标注；（3）随机基线未适配任务特性；（4）仅以体素数衡量标注成本。本文提出nnActive，一个开源框架，通过（1）覆盖四个生物医学影像数据集和三种标注策略的大规模研究；（2）扩展nnU-Net，使用部分标注进行3D块式查询选择；（3）提出前景感知随机采样以应对医学图像中前景-背景类别不平衡；（4）引入前景效率指标，捕捉背景区域低标注成本。结果发现：（A）所有AL方法优于标准随机采样，但均未稳定超越改进的前景感知随机采样；（B）AL收益依赖任务特定参数；（C）预测熵总体表现最佳，但需最多标注投入；（D）更多计算资源可提升AL性能。nnActive作为完整、开源框架，可推动3D生物医学成像中主动学习的研究与应用。代码见：https://github.com/MIC-DKFZ/nnActive
原文摘要 · Abstract (English)
Semantic segmentation is crucial for various biomedical applications, yet its reliance on large annotated datasets presents a bottleneck due to the high cost and specialized expertise required for manual labeling. Active Learning (AL) aims to mitigate this challenge by querying only the most informative samples, thereby reducing annotation effort. However, in the domain of 3D biomedical imaging, there is no consensus on whether AL consistently outperforms Random sampling. Four evaluation pitfalls hinder the current methodological assessment. These are (1) restriction to too few datasets and annotation budgets, (2) using 2D models on 3D images without partial annotations, (3) Random baseline not being adapted to the task, and (4) measuring annotation cost only in voxels. In this work, we introduce nnActive, an open-source AL framework that overcomes these pitfalls by (1) means of a large scale study spanning four biomedical imaging datasets and three label regimes, (2) extending nnU-Net by using partial annotations for training with 3D patch-based query selection, (3) proposing Foreground Aware Random sampling strategies tackling the foreground-background class imbalance of medical images and (4) propose the foreground efficiency metric, which captures the low annotation cost of background-regions. We reveal the following findings: (A) while all AL methods outperform standard Random sampling, none reliably surpasses an improved Foreground Aware Random sampling; (B) benefits of AL depend on task specific parameters; (C) Predictive Entropy is overall the best performing AL method, but likely requires the most annotation effort; (D) AL performance can be improved with more compute intensive design choices. As a holistic, open-source framework, nnActive can serve as a catalyst for research and application of AL in 3D biomedical imaging. Code is at: https://github.com/MIC-DKFZ/nnActive
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。