Visualizing Multimodal Deep Learning for Lesion Prediction | IEEE Journals & Magazine | IEEE Xplore

Visualizing Multimodal Deep Learning for Lesion Prediction


Abstract:

A U-Net is a type of convolutional neural network that has been shown to output impressive results in medical imaging segmentation tasks. Still, neural networks in genera...Show More

Abstract:

A U-Net is a type of convolutional neural network that has been shown to output impressive results in medical imaging segmentation tasks. Still, neural networks in general form a black box that is hard to interpret, especially by noncomputer scientists. This work provides a visual system that allows users to examine U-Nets that were trained to predict brain lesions caused by stroke using multimodal imaging. We provide several visualization views that allow users to load trained U-Nets, run them on different patient data, and examine the results while visually following the computation of the U-Net. With these visualizations, we can provide useful information for our medical collaborators showing how the training database can be improved and which features are best learned by the neural network.
Published in: IEEE Computer Graphics and Applications ( Volume: 41, Issue: 5, 01 Sept.-Oct. 2021)
Page(s): 90 - 98
Date of Publication: 10 September 2021

ISSN Information:

PubMed ID: 34506270

References

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