[Faculty] Fwd: [CSRC.COLLOQUIUM] "Interpretation of Radiological Imaging Features using Generative Adversarial Networks "

[Jose Castillo]

[image: SDSU_CSRC Logo.jpg]

DATE:
*Friday, October 8, 2021*


TITLE:
*Interpretation of Radiological Imaging Features using Generative
Adversarial Networks  *

TIME:
*3:30-4:30PM*



LOCATION:
*In Person - GMCS 314*
or
Join Zoom Meeting -   https://SDSU.zoom.us/j/86808277973
<https://sdsu.zoom.us/j/86808277973>


SPEAKER/BIO:
*Kyle Hasenstab, Mathematics and Statistics, San Diego State University *



ABSTRACT:

Convolutional neural networks (CNNs) have become valuable instruments for
computer vision in medical imaging, able to learn features of disease
without explicit programming. However, algorithm transparency is necessary
for these to be applied in clinical practice. To address this, we propose a
feature interpretation generative adversarial network (FIGAN) to generate
synthetic images that facilitate CNN feature interpretation. Feasibility of
the proposed approach was assessed on a previously-developed CNN designed
to assess contrast enhancement adequacy of liver MR images for lesion
detection. Review of FIGAN images revealed that this CNN utilizes features
related to tissue-vessel contrast, nodular liver texture, and tissue
brightness to determine adequacy of contrast enhancement.


Host:
*Jose Castillo*

Note: Videos of previous colloquium talks can be seen on the CSRC website
in the colloquium archive section or on the CSRC YouTube page here
<https://www.youtube.com/channel/UCN0ZEztlmyDqG2pm-Rle_Eg/feed>.




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