A Benchmark for Weakly Semi-Supervised Abnormality Localization in Chest X-Rays

TL;DR

This paper introduces a weakly semi-supervised framework for abnormality localization in chest X-rays, leveraging limited fully annotated data and extensive point annotations to improve accuracy with minimal labeling effort.

Contribution

The proposed Point Beyond Class (PBC) method effectively maps point annotations to bounding boxes using novel regularization and self-supervision, reducing annotation costs while maintaining high localization accuracy.

Findings

Achieves ~5 mAP improvement with less than 20% fully labeled data.

Outperforms state-of-the-art Point DETR in weakly supervised settings.

Validated on RSNA and VinDr-CXR datasets.

Abstract

Accurate abnormality localization in chest X-rays (CXR) can benefit the clinical diagnosis of various thoracic diseases. However, the lesion-level annotation can only be performed by experienced radiologists, and it is tedious and time-consuming, thus difficult to acquire. Such a situation results in a difficulty to develop a fully-supervised abnormality localization system for CXR. In this regard, we propose to train the CXR abnormality localization framework via a weakly semi-supervised strategy, termed Point Beyond Class (PBC), which utilizes a small number of fully annotated CXRs with lesion-level bounding boxes and extensive weakly annotated samples by points. Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost. Particularly, the core idea behind our PBC is to learn a robust and accurate mapping from the point annotations to the bounding boxes against the variance of annotated points. To achieve that, a regularization term, namely multi-point consistency, is proposed, which drives the model to generate the consistent bounding box from different point annotations inside the same abnormality. Furthermore, a self-supervision, termed symmetric consistency, is also proposed to deeply exploit the useful information from the weakly annotated data for abnormality localization. Experimental results on RSNA and VinDr-CXR datasets justify the effectiveness of the proposed method. When less than 20% box-level labels are used for training, an improvement of ~5 in mAP can be achieved by our PBC, compared to the current state-of-the-art method (i.e., Point DETR). Code is available at https://github.com/HaozheLiu-ST/Point-Beyond-Class.