Lung Segmentation from Chest X-rays using Variational Data Imputation

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.
OriginalsprogEngelsk
TitelICML Workshop on Learning with Missing Values
Publikationsdato20 maj 2020
StatusUdgivet - 20 maj 2020
BegivenhedICML Workshop on Learning with Missing Values - Virtual
Varighed: 17 jul. 2020 → …
https://openreview.net/group?id=ICML.cc/2020/Workshop/Artemiss

Workshop

WorkshopICML Workshop on Learning with Missing Values
LokationVirtual
Periode17/07/2020 → …
Internetadresse
NavnarXiv

Bibliografisk note

Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE/

    Forskningsområder

  • eess.IV, cs.CV, cs.LG, stat.ML

ID: 244567152