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

by   Haoqin Ji, et al.

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 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.


page 1

page 2

page 3

page 4


Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters

Weakly-supervised object detection (WSOD) has emerged as an inspiring re...

Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling

Localizing thoracic diseases on chest X-ray plays a critical role in cli...

Object Localization under Single Coarse Point Supervision

Point-based object localization (POL), which pursues high-performance ob...

Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotation

Convolutional neural networks (CNNs) have been successfully applied to c...

BDC: Bounding-Box Deep Calibration for High Performance Face Detection

Modern CNN-based face detectors have achieved tremendous strides due to ...

LOOC: Localize Overlapping Objects with Count Supervision

Acquiring count annotations generally requires less human effort than po...

A dual branch deep neural network for classification and detection in mammograms

In this paper, we propose a novel deep learning architecture for joint c...

Please sign up or login with your details

Forgot password? Click here to reset