Reference : Towards Generalizable Machine Learning for Chest X-ray Diagnosis with Multi-task learning
E-prints/Working papers : First made available on ORBilu
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/50815
Towards Generalizable Machine Learning for Chest X-ray Diagnosis with Multi-task learning
English
Ghamizi, Salah mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Garcia Santa Cruz, Beatriz mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Temple, Paul mailto []
Cordy, Maxime mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
Perrouin, Gilles mailto []
Papadakis, Mike mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Le Traon, Yves mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal >]
Feb-2022
18
No
[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.
http://hdl.handle.net/10993/50815
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):

FileCommentaryVersionSizeAccess
Open access
Towards Generalizable Machine Learning for Chest X-ray Diagnosis with Multitask Learning.pdfPublisher postprint4.01 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.