167 research outputs found
Lung Segmentation from Chest X-rays using Variational Data Imputation
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.Comment: Accepted to be presented at the first Workshop on the Art of Learning
with Missing Values (Artemiss) hosted by the 37th International Conference on
Machine Learning (ICML). Source code, training data and the trained models
are available here: https://github.com/raghavian/lungVAE
Explaining the Black-box Smoothly- A Counterfactual Approach
We propose a BlackBox \emph{Counterfactual Explainer} that is explicitly
developed for medical imaging applications. Classical approaches (e.g. saliency
maps) assessing feature importance do not explain \emph{how} and \emph{why}
variations in a particular anatomical region is relevant to the outcome, which
is crucial for transparent decision making in healthcare application. Our
framework explains the outcome by gradually \emph{exaggerating} the semantic
effect of the given outcome label. Given a query input to a classifier,
Generative Adversarial Networks produce a progressive set of perturbations to
the query image that gradually changes the posterior probability from its
original class to its negation. We design the loss function to ensure that
essential and potentially relevant details, such as support devices, are
preserved in the counterfactually generated images. We provide an extensive
evaluation of different classification tasks on the chest X-Ray images. Our
experiments show that a counterfactually generated visual explanation is
consistent with the disease's clinical relevant measurements, both
quantitatively and qualitatively.Comment: Under review for IEEE-TMI journa
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