Log In Sign Up

Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning

by   Gangming Zhao, et al.

Locating lesions is important in the computer-aided diagnosis of X-ray images. However, box-level annotation is time-consuming and laborious. How to locate lesions accurately with few, or even without careful annotations is an urgent problem. Although several works have approached this problem with weakly-supervised methods, the performance needs to be improved. One obstacle is that general weakly-supervised methods have failed to consider the characteristics of X-ray images, such as the highly-structural attribute. We therefore propose the Cross-chest Graph (CCG), which improves the performance of automatic lesion detection by imitating doctor's training and decision-making process. CCG models the intra-image relationship between different anatomical areas by leveraging the structural information to simulate the doctor's habit of observing different areas. Meanwhile, the relationship between any pair of images is modeled by a knowledge-reasoning module to simulate the doctor's habit of comparing multiple images. We integrate intra-image and inter-image information into a unified end-to-end framework. Experimental results on the NIH Chest-14 database (112,120 frontal-view X-ray images with 14 diseases) demonstrate that the proposed method achieves state-of-the-art performance in weakly-supervised localization of lesions by absorbing professional knowledge in the medical field.


page 3

page 7

page 8


GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray images

Locating diseases in chest X-ray images with few careful annotations sav...

ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays

Image representation is a fundamental task in computer vision. However, ...

Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance

Although deep learning algorithms have been intensively developed for co...

OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays

Chest X-ray (CXR) is the most typical medical image worldwide to examine...

Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis

During clinical practice, radiologists often use attributes, e.g. morpho...

Weakly-Supervised Localization and Classification of Proximal Femur Fractures

In this paper, we target the problem of fracture classification from cli...

Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays

Given image labels as the only supervisory signal, we focus on harvestin...