A Sneak Attack on Segmentation of Medical Images Using Deep Neural
Network Classifiers
- URL: http://arxiv.org/abs/2201.02771v1
- Date: Sat, 8 Jan 2022 05:57:26 GMT
- Title: A Sneak Attack on Segmentation of Medical Images Using Deep Neural
Network Classifiers
- Authors: Shuyue Guan, Murray Loew
- Abstract summary: We use trained Convolutional Neural Network (CNN) classifiers to approach the segmentation problem.
Heatmaps can be visualized and formed using Gradient-weighted Class Activation Mapping (Grad-CAM)
Results from our experiments show that heatmaps can locate and segment partial tumor areas.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Instead of using current deep-learning segmentation models (like the UNet and
variants), we approach the segmentation problem using trained Convolutional
Neural Network (CNN) classifiers, which automatically extract important
features from classified targets for image classification. Those extracted
features can be visualized and formed heatmaps using Gradient-weighted Class
Activation Mapping (Grad-CAM). This study tested whether the heatmaps could be
used to segment the classified targets. We also proposed an evaluation method
for the heatmaps; that is, to re-train the CNN classifier using images filtered
by heatmaps and examine its performance. We used the mean-Dice coefficient to
evaluate segmentation results. Results from our experiments show that heatmaps
can locate and segment partial tumor areas. But only use of the heatmaps from
CNN classifiers may not be an optimal approach for segmentation. In addition,
we have verified that the predictions of CNN classifiers mainly depend on tumor
areas, and dark regions in Grad-CAM's heatmaps also contribute to
classification.
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