不确定性感知助力数字病理学中癌症亚型分类的高效标注
Uncertainty Awareness Enables Efficient Labeling for Cancer Subtyping in Digital Pathology
AI总结：
针对数字病理癌症亚型分类需大量专家标注的问题，研究将不确定性感知引入自监督对比学习模型，通过不确定性分数选关键图像标注，仅用1-10%标注即达基准数据集SOTA，提升分类精度与效率。
摘要：
机器学习辅助的癌症亚型分类是数字病理学领域极具前景的方向。然而，癌症亚型分类模型需要利用专家标注进行精细训练，才能在具备一定已知确定性（或不确定性）的前提下完成推理。为此，我们将不确定性感知的概念引入自监督对比学习模型中。具体实现方式为：在每个训练轮次计算一个证据向量，用于评估模型对其预测结果的置信度。随后，推导得到的不确定性分数被用作指标，对需要进一步标注的最关键图像进行选择性标注，从而迭代优化训练过程。仅通过1-10%的策略性筛选标注数据，我们就在多个基准数据集上取得了癌症亚型分类的最优性能。我们的方法不仅能策略性地引导标注流程，降低对大规模标注数据集的需求，还能提升分类的精度与效率。这一进展在标注数据有限的场景中尤为适用，为数字病理学领域的未来研究与应用提供了极具潜力的方向。
英文摘要：
Machine-learning-assisted cancer subtyping is a promising avenue in digital pathology. Cancer subtyping models, however, require careful training using expert annotations so that they can be inferred with a degree of known certainty (or uncertainty). To this end, we introduce the concept of uncertainty awareness into a self-supervised contrastive learning model. This is achieved by computing an evidence vector at every epoch, which assesses the model's confidence in its predictions. The derived uncertainty score is then utilized as a metric to selectively label the most crucial images that require further annotation, thus iteratively refining the training process. With just 1-10% of strategically selected annotations, we attain state-of-the-art performance in cancer subtyping on benchmark datasets. Our method not only strategically guides the annotation process to minimize the need for extensive labeled datasets, but also improves the precision and efficiency of classifications. This development is particularly beneficial in settings where the availability of labeled data is limited, offering a promising direction for future research and application in digital pathology.
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。
