  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.
