bibtext: @InProceedings{ Yan_TSBP_MICCAI2024,
                   author = { Yang, Tingting and Xiao, Liang and Zhang, Yizhe },
                   title = { { TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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.
title: TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation
authors: Yang, Tingting Xiao, Liang Zhang, Yizhe