14 research outputs found
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
Deep learning has now become the de facto approach to the recognition of
anomalies in medical imaging. Their 'black box' way of classifying medical
images into anomaly labels poses problems for their acceptance, particularly
with clinicians. Current explainable AI methods offer justifications through
visualizations such as heat maps but cannot guarantee that the network is
focusing on the relevant image region fully containing the anomaly. In this
paper, we develop an approach to explainable AI in which the anomaly is assured
to be overlapping the expected location when present. This is made possible by
automatically extracting location-specific labels from textual reports and
learning the association of expected locations to labels using a hybrid
combination of Bi-Directional Long Short-Term Memory Recurrent Neural Networks
(Bi-LSTM) and DenseNet-121. Use of this expected location to bias the
subsequent attention-guided inference network based on ResNet101 results in the
isolation of the anomaly at the expected location when present. The method is
evaluated on a large chest X-ray dataset.Comment: 5 pages, Paper presented as a poster at the International Symposium
on Biomedical Imaging, 2020, Paper Number 65
Towards Automatic Prediction of Outcome in Treatment of Cerebral Aneurysms
Intrasaccular flow disruptors treat cerebral aneurysms by diverting the blood
flow from the aneurysm sac. Residual flow into the sac after the intervention
is a failure that could be due to the use of an undersized device, or to
vascular anatomy and clinical condition of the patient. We report a machine
learning model based on over 100 clinical and imaging features that predict the
outcome of wide-neck bifurcation aneurysm treatment with an intravascular
embolization device. We combine clinical features with a diverse set of common
and novel imaging measurements within a random forest model. We also develop
neural network segmentation algorithms in 2D and 3D to contour the sac in
angiographic images and automatically calculate the imaging features. These
deliver 90% overlap with manual contouring in 2D and 83% in 3D. Our predictive
model classifies complete vs. partial occlusion outcomes with an accuracy of
75.31%, and weighted F1-score of 0.74.Comment: 10 page