面向将组织病理学显微图像分类为时间序列数据
Towards Classifying Histopathological Microscope Images as Time Series Data
AI总结：
本文提出将显微病理图像视为时间序列，利用动态时间规整和注意力池化进行分类，在多种基线上验证了有效性，并提升了医学图像分析的可靠性。
摘要：
作为癌症诊断的一线数据，显微病理图像对于为患者提供快速准确的治疗至关重要。然而，尽管其具有实际价值，深度学习社区在很大程度上忽视了其应用。本文提出了一种将显微图像分类为时间序列数据的新方法，以应对其人工采集和弱标注特性所带来的独特挑战。所提出的方法通过利用动态时间规整（DTW）将不同长度的图像序列拟合到固定长度的目标上。同时采用基于注意力的池化来预测病例的类别。我们通过与各种基线方法的性能比较，并展示使用各种推理策略在获得稳定可靠结果方面的优势，证明了我们方法的有效性。消融研究进一步验证了每个组件的贡献。我们的方法不仅接纳了显微图像，还将其性能提升到可信赖的水平，从而为医学图像分析做出了贡献。
英文摘要：
As the frontline data for cancer diagnosis, microscopic pathology images are fundamental for providing patients with rapid and accurate treatment. However, despite their practical value, the deep learning community has largely overlooked their usage. This paper proposes a novel approach to classifying microscopy images as time series data, addressing the unique challenges posed by their manual acquisition and weakly labeled nature. The proposed method fits image sequences of varying lengths to a fixed-length target by leveraging Dynamic Time-series Warping (DTW). Attention-based pooling is employed to predict the class of the case simultaneously. We demonstrate the effectiveness of our approach by comparing performance with various baselines and showcasing the benefits of using various inference strategies in achieving stable and reliable results. Ablation studies further validate the contribution of each component. Our approach contributes to medical image analysis by not only embracing microscopic images but also lifting them to a trustworthy level of performance.
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。
