用点标注估算熵，95%减少标注量仍保持良好细胞检测效果。
Entropy Bootstrapping for Weakly Supervised Nuclei Detection
- 通过点标签估计像素熵，推断细胞分布生成完整掩码
- 仅需全标注1/20的像素标签，性能接近全标注方法
- 适合标注成本高的医学图像分割任务
显微图像中的结构分割（如细胞或细胞核检测）通常需要人工绘制每个实例的精确轮廓。弱监督方法（如仅使用单点标签）有望显著降低标注工作量。本文方法利用单个点标签估算像素熵，以近似细胞像素的潜在分布，并据此推断出完整的细胞掩码，再通过Mask-RCNN实现实例分割。与使用完整标注掩码训练相比，本方法在仅减少95%像素标注的前提下，仍达到相当的性能水平。
原文摘要 · Abstract (English)
Microscopy structure segmentation, such as detecting cells or nuclei, generally requires a human to draw a ground truth contour around each instance. Weakly supervised approaches (e.g. consisting of only single point labels) have the potential to reduce this workload significantly. Our approach uses individual point labels for an entropy estimation to approximate an underlying distribution of cell pixels. We infer full cell masks from this distribution, and use Mask-RCNN to produce an instance segmentation output. We compare this point--annotated approach with training on the full ground truth masks. We show that our method achieves a comparatively good level of performance, despite a 95% reduction in pixel labels.
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