Exploiting well-labeled training sets has led deep learning models to astonishing results for counting biological structures in microscopy images. However, dealing with weak multi-rater annotations, i.e., when multiple human raters disagree due to non-trivial patterns, remains a relatively unexplored problem. More reliable labels can be obtained by aggregating and averaging the decisions given by several raters to the same data. Still, the scale of the counting task and the limited budget for labeling prohibit this. As a result, making the most with small quantities of multi-rater data is crucial. To this end, we propose a two-stage counting strategy in a weakly labeled data scenario. First, we detect and count the biological structures; then, in the second step, we refine the predictions, increasing the correlation between the scores assigned to the samples and the raters’ agreement on the annotations. We assess our methodology on a novel dataset comprising fluorescence microscopy images of mice brains containing extracellular matrix aggregates named perineuronal nets. We demonstrate that we significantly enhance counting performance, improving confidence calibration by taking advantage of the redundant information characterizing the small sets of available multi-rater data.

利用标记良好的训练集使深度学习模型在显微镜图像中计数生物结构方面取得了惊人的结果。然而，处理弱的多评价者注释，即当多个人类评价者由于非平凡的模式不同意时，仍然是一个相对未探索的问题。通过聚合和平均几个评估者对相同数据给出的决定，可以获得更可靠的标签。尽管如此，计数任务的规模和有限的标签预算仍然阻止了这一点。因此，充分利用少量的多评估者数据至关重要。为此，我们在弱标记数据场景中提出了一种两阶段计数策略。首先，我们检测和计算生物结构；然后，在第二步中，我们改进预测，增加分配给样本的分数与评估者对注释的一致性之间的相关性。我们在一个新的数据集上评估我们的方法，该数据集包括小鼠大脑的荧光显微镜图像，这些图像包含称为神经周围网的细胞外基质聚集体。我们证明了我们显着提高了计数性能，通过利用表征少量可用多评估者数据的冗余信息来提高置信度校准。