A Relational-learning Perspective to Multi-label Chest X-ray Classification

03/10/2021
by   Anjany Sekuboyina, et al.
0

Multi-label classification of chest X-ray images is frequently performed using discriminative approaches, i.e. learning to map an image directly to its binary labels. Such approaches make it challenging to incorporate auxiliary information such as annotation uncertainty or a dependency among the labels. Building towards this, we propose a novel knowledge graph reformulation of multi-label classification, which not only readily increases predictive performance of an encoder but also serves as a general framework for introducing new domain knowledge. Specifically, we construct a multi-modal knowledge graph out of the chest X-ray images and its labels and pose multi-label classification as a link prediction problem. Incorporating auxiliary information can then simply be achieved by adding additional nodes and relations among them. When tested on a publicly-available radiograph dataset (CheXpert), our relational-reformulation using a naive knowledge graph outperforms the state-of-art by achieving an area-under-ROC curve of 83.5 discriminative approach.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/20/2021

AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray

Radiologists usually observe anatomical regions of chest X-ray images as...
research
02/06/2023

Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs

This study explores the use of the Dirichlet Variational Autoencoder (Di...
research
07/30/2020

Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge

Multi-label zero-shot classification aims to predict multiple unseen cla...
research
07/11/2022

Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency

Many real-world applications of image recognition require multi-label le...
research
03/06/2018

Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

The increased availability of X-ray image archives (e.g. the ChestX-ray1...
research
06/06/2020

Deep Mining External Imperfect Data for Chest X-ray Disease Screening

Deep learning approaches have demonstrated remarkable progress in automa...
research
10/28/2017

Learning to diagnose from scratch by exploiting dependencies among labels

The field of medical diagnostics contains a wealth of challenges which c...

Please sign up or login with your details

Forgot password? Click here to reset