Imaging mass cytometry (IMC) is a relatively new technique for imaging
biological tissue at subcellular resolution. In recent years, learning-based
segmentation methods have enabled precise quantification of cell type and
morphology, but typically rely on large datasets with fully annotated ground
truth (GT) labels. This paper explores the effects of imperfect labels on
learning-based segmentation models and evaluates the generalisability of these
models to different tissue types. Our results show that removing 50% of cell
annotations from GT masks only reduces the dice similarity coefficient (DSC)
score to 0.874 (from 0.889 achieved by a model trained on fully annotated GT
masks). This implies that annotation time can in fact be reduced by at least
half without detrimentally affecting performance. Furthermore, training our
single-tissue model on imperfect labels only decreases DSC by 0.031 on an
unseen tissue type compared to its multi-tissue counterpart, with negligible
qualitative differences in segmentation. Additionally, bootstrapping the
worst-performing model (with 5% of cell annotations) a total of ten times
improves its original DSC score of 0.720 to 0.829. These findings imply that
less time and work can be put into the process of producing comparable
segmentation models; this includes eliminating the need for multiple IMC tissue
types during training, whilst also providing the potential for models with very
few labels to improve on themselves. Source code is available on GitHub:
this https URL

通过去除细胞注释中的 50% ，学习性的分割模型表现的结果只比完整的标签相应的模型降低了 DSC 指标，表明可以至少减少一半的注释时间而不影响性能，并且单一组织模型与多组织模型在新颖组织类型上的性能差异微乎其微，并且通过多次增加最差性能模型的训练可以提高其性能。

推动细胞分割模型在成像质谱细胞学中的极限

Pushing the limits of cell segmentation models for imaging mass  cytometry

This study demonstrates a cost-effective approach to semantic segmentation
using self-supervised vision transformers (SSVT). By freezing the SSVT backbone
and training a lightweight segmentation head, our approach effectively utilizes
imperfect labels, thereby improving robustness to label imperfections.
Empirical experiments show significant performance improvements over existing
methods for various annotation types, including scribble, point-level, and
image-level labels. The research highlights the effectiveness of
self-supervised vision transformers in dealing with imperfect labels, providing
a practical and efficient solution for semantic segmentation while reducing
annotation costs. Through extensive experiments, we confirm that our method
outperforms baseline models for all types of imperfect labels. Especially under
the zero-shot vision-language-model-based label, our model exhibits 11.5\%p
performance gain compared to the baseline.

通过冻结自监督视觉变换器（SSVT）主干并训练轻量级分割头，我们展示了一种成本效益的语义分割方法。通过利用不完美的标签，我们的方法提高了对标签缺陷的鲁棒性，对于各种注释类型，包括涂鸦、点层和图像层标签，在现有方法上展现了显著的性能改进。这项研究突出了自监督视觉变换器在处理不完美标签方面的有效性，为语义分割提供了实用且高效的解决方案，同时降低了注释成本。通过大量实验，我们确认我们的方法在所有类型的不完美标签上优于基准模型，特别在基于零样本视觉语言模型的标签下，相对于基准模型展现了 11.5% 的性能提升。

自我监督视觉变换器是不完善标签的高效分割学习器

Self-Supervised Vision Transformers Are Efficient Segmentation Learners  for Imperfect Labels

Density estimation is one of the most widely used methods for crowd counting
in which a deep learning model learns from head-annotated crowd images to
estimate crowd density in unseen images. Typically, the learning performance of
the model is highly impacted by the accuracy of the annotations and inaccurate
annotations may lead to localization and counting errors during prediction. A
significant amount of works exist on crowd counting using perfectly labelled
datasets but none of these explore the impact of annotation errors on the model
accuracy. In this paper, we investigate the impact of imperfect labels (both
noisy and missing labels) on crowd counting accuracy. We propose a system that
automatically generates imperfect labels using a deep learning model (called
annotator) which are then used to train a new crowd counting model (target
model). Our analysis on two crowd counting models and two benchmark datasets
shows that the proposed scheme achieves accuracy closer to that of the model
trained with perfect labels showing the robustness of crowd models to
annotation errors.

本文研究了深度学习模型在使用不完美标注的人群图像数据进行密度估算时的鲁棒性，提出了一种使用自动生成的不完美标注数据训练的方法以代替标注错误数据的方法。实验结果表明，该方法可以更接近于使用完美标注数据训练的人群计数模型的准确度。