全视野切片图像语义分割中主动学习的自适应区域选择
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation
AI总结：
针对WSI分割中区域主动学习步长难调的问题，提出先定位信息区域再检测最优边界框的自适应选择方法，在CAMELYON16上仅标注2.6%组织面积即达到全标注性能。
摘要：
为训练监督分割模型而在像素级标注组织学千兆像素大小的全视野切片图像（WSI）是一项耗时的过程。基于区域的主动学习（AL）并不请求对整幅图像进行标注，而是在数量有限的已标注图像区域上训练模型。这些标注区域会被迭代选择，目标是在最小化已标注面积的同时优化模型性能。区域选择的标准方法会评估指定大小的所有方形区域的信息量，然后选择特定数量的最具信息量区域。我们发现，该方法的效率高度依赖于 AL 步长的选择（即区域大小与每个 WSI 所选区域数量的组合），而次优的 AL 步长可能导致冗余的标注请求或膨胀的计算成本。本文介绍了一种自适应选择标注区域的新技术，以减轻对该 AL 超参数的依赖。具体而言，我们通过先识别一个信息丰富的区域，再检测其最优边界框，来动态确定每个区域，而不是像标准方法那样选择具有统一预定义形状和大小的区域。我们在公开的 CAMELYON16 数据集上使用乳腺癌转移灶分割任务评估了所提方法，并表明在各种 AL 步长下，该方法始终比标准方法获得更高的采样效率。仅标注 2.6% 的组织面积，我们就达到了全标注性能，从而大幅降低了标注 WSI 数据集的成本。源代码可在 https://github.com/DeepMicroscopy/AdaptiveRegionSelection 获取。
英文摘要：
The process of annotating histological gigapixel-sized whole slide images (WSIs) at the pixel level for the purpose of training a supervised segmentation model is time-consuming. Region-based active learning (AL) involves training the model on a limited number of annotated image regions instead of requesting annotations of the entire images. These annotation regions are iteratively selected, with the goal of optimizing model performance while minimizing the annotated area. The standard method for region selection evaluates the informativeness of all square regions of a specified size and then selects a specific quantity of the most informative regions. We find that the efficiency of this method highly depends on the choice of AL step size (i.e., the combination of region size and the number of selected regions per WSI), and a suboptimal AL step size can result in redundant annotation requests or inflated computation costs. This paper introduces a novel technique for selecting annotation regions adaptively, mitigating the reliance on this AL hyperparameter. Specifically, we dynamically determine each region by first identifying an informative area and then detecting its optimal bounding box, as opposed to selecting regions of a uniform predefined shape and size as in the standard method. We evaluate our method using the task of breast cancer metastases segmentation on the public CAMELYON16 dataset and show that it consistently achieves higher sampling efficiency than the standard method across various AL step sizes. With only 2.6\% of tissue area annotated, we achieve full annotation performance and thereby substantially reduce the costs of annotating a WSI dataset. The source code is available at https://github.com/DeepMicroscopy/AdaptiveRegionSelection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
