Towards Classifying Histopathological Microscope Images as Time Series Data
This work addresses the challenge of cancer diagnosis using microscopy images, which are often overlooked in deep learning, by proposing a novel classification approach, though it appears incremental in medical image analysis.
The paper tackled the problem of classifying weakly labeled histopathological microscope images by treating them as time series data, achieving stable and reliable results through a method that includes Dynamic Time-series Warping and attention-based pooling.
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.