Reference : Towards Generalizable Machine Learning for Chest X-ray Diagnosis with Multi-task learning

	Document type :	E-prints/Working papers : First made available on ORBilu
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/50815

Title :	Towards Generalizable Machine Learning for Chest X-ray Diagnosis with Multi-task learning
Language :	English
Author, co-author :	Ghamizi, Salah [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Garcia Santa Cruz, Beatriz [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
	Temple, Paul []
	Cordy, Maxime [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
	Perrouin, Gilles []
	Papadakis, Mike [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
	Le Traon, Yves [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
Publication date :	Feb-2022
Number of pages :	18
Peer reviewed :	No
Abstract :	[en] Clinicians use chest radiography (CXR) to diagnose common pathologies. Automated classification of these diseases can expedite analysis workflow, scale to growing numbers of patients and reduce healthcare costs. While research has produced classification models that perform well on a given dataset, the same models lack generalization on different datasets. This reduces confidence that these models can be reliably deployed across various clinical settings. We propose an approach based on multitask learning to improve model generalization. We demonstrate that learning a (main) pathology together with an auxiliary pathology can significantly impact generalization performance (between -10% and +15% AUC-ROC). A careful choice of auxiliary pathology even yields competitive performance with state-of-the-art models that rely on fine-tuning or ensemble learning, using between 6% and 34% of the training data that these models required. We, further, provide a method to determine what is the best auxiliary task to choose without access to the target dataset. Ultimately, our work makes a big step towards the creation of CXR diagnosis models applicable in the real world, through the evidence that multitask learning can drastically improve generalization.
Permalink :	http://hdl.handle.net/10993/50815
FnR project :	FnR ; FNR12669767 > Yves Le Traon > STELLAR > Testing Self-learning Systems > 01/09/2019 > 31/08/2022 > 2018

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access Towards Generalizable Machine Learning for Chest X-ray Diagnosis with Multitask Learning.pdf Publisher postprint 4.01 MB View/Open

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map