TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation

TL;DR

This paper introduces TSBP, a test-time method that improves object detection in histology images by propagating bounding box confidence using visual similarity, without requiring extra training data.

Contribution

TSBP leverages Earth Mover's Distance for self-guided bounding box propagation, enhancing detection accuracy without additional labeled samples.

Findings

TSBP outperforms simple thresholding in gland and cell detection tasks.

The method improves robustness and accuracy of detection results.

No extra labeled data needed for implementation.

Abstract

A global threshold (e.g., 0.5) is often applied to determine which bounding boxes should be included in the final results for an object detection task. A higher threshold reduces false positives but may result in missing a significant portion of true positives. A lower threshold can increase detection recall but may also result in more false positives. Because of this, using a preset global threshold (e.g., 0.5) applied to all the bounding box candidates may lead to suboptimal solutions. In this paper, we propose a Test-time Self-guided Bounding-box Propagation (TSBP) method, leveraging Earth Mover's Distance (EMD) to enhance object detection in histology images. TSBP utilizes bounding boxes with high confidence to influence those with low confidence, leveraging visual similarities between them. This propagation mechanism enables bounding boxes to be selected in a controllable, explainable, and robust manner, which surpasses the effectiveness of using simple thresholds and uncertainty calibration methods. Importantly, TSBP does not necessitate additional labeled samples for model training or parameter estimation, unlike calibration methods. We conduct experiments on gland detection and cell detection tasks in histology images. The results show that our proposed TSBP significantly improves detection outcomes when working in conjunction with state-of-the-art deep learning-based detection networks. Compared to other methods such as uncertainty calibration, TSBP yields more robust and accurate object detection predictions while using no additional labeled samples. The code is available at https://github.com/jwhgdeu/TSBP.