Towards Classifying Histopathological Microscope Images as Time Series Data

TL;DR

This paper introduces a novel method for classifying microscopy images as time series data using Dynamic Time-series Warping and attention-based pooling, improving reliability and performance in medical image analysis.

Contribution

It presents a new approach that treats microscopy images as time series, addressing challenges of manual acquisition and weak labels, with validation through extensive experiments.

Findings

Effective classification performance surpassing baselines

Stable results with various inference strategies

Component ablation confirms method's robustness

Abstract

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.