2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDCOVID-IP-1.7
Paper Title FEATURES OF ICU ADMISSION IN X-RAY IMAGES OF COVID-19 PATIENTS
Authors Douglas Pinto Sampaio Gomes, Anwaar Ulhaq, Manoranjan Paul, Machine Vision and Digital Health (MAVIDH) Research group, Charles Sturt University, Australia; Michael Horry, Subrata Chakraborty, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Australia; Manash Saha, Manning Rural Referral Hospital, Australia; Tanmoy Debnath, Machine Vision and Digital Health (MAVIDH) Research group, Charles Sturt University, Australia; D.M. Motiur Rahaman, Charles Sturt University, Australia
SessionCOVID-IP-1: COVID Related Image Processing 1
LocationArea C
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic COVID-Related Image Processing: COVID-related image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents an original methodology for extracting semantic features from X-rays images that correlate to severity from a data set with patient ICU admission labels through interpretable models. The validation is partially performed by a proposed method that correlates the extracted features with a separate larger data set that does not contain the ICU-outcome labels. The analysis points out that a few features explain most of the variance between patients admitted in ICUs or not. The methods herein can be viewed as a statistical approach highlighting the importance of features related to ICU admission that may have been only qualitatively reported. In between features shown to be over-represented in the external data set were ones like ‘Consolidation’ (1.67), ‘Alveolar’ (1.33), and ‘Effusion’ (1.3). A brief analysis on the locations also showed higher frequency in labels like ‘Bilateral’ (1.58) and Peripheral (1.28) in patients labelled with higher chances to be admitted in ICU. To properly handle the limited data sets, a state-of-the-art lung segmentation network was also trained and presented, together with the use of low-complexity and interpretable models to avoid overfitting.