将病理显微图像当作时间序列处理，提升癌症诊断准确率
Towards Classifying Histopathological Microscope Images as Time Series Data
- 用动态时间规整对长短不一的图像序列做对齐
- 结合注意力池化实现病例级分类，性能优于多个基线
- 适合医疗图像分析、弱标签数据研究者参考
作为癌症诊断的一线数据，显微病理图像对患者快速准确治疗至关重要。然而，尽管其实际价值突出，深度学习领域对其应用仍关注不足。本文提出一种新方法，将显微图像序列视为时间序列进行分类，解决其人工获取和弱标签带来的挑战。通过动态时间规整（DTW）将长度各异的图像序列对齐至固定长度，并采用基于注意力的池化机制同时预测病例类别。实验对比多种基线模型，验证了该方法的有效性，并展示了不同推理策略在获得稳定可靠结果中的优势。消融实验进一步确认了各模块的贡献。本方法不仅拓展了显微图像的应用范围，更将其性能提升至可信赖水平。
原文摘要 · Abstract (English)
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.
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