TransRUPNet for Improved Polyp Segmentation
arxiv(2023)
摘要
Colorectal cancer is among the most common cause of cancer worldwide. Removal
of precancerous polyps through early detection is essential to prevent them
from progressing to colon cancer. We develop an advanced deep learning-based
architecture, Transformer based Residual Upsampling Network (TransRUPNet) for
automatic and real-time polyp segmentation. The proposed architecture,
TransRUPNet, is an encoder-decoder network consisting of three encoder and
decoder blocks with additional upsampling blocks at the end of the network.
With the image size of 256×256, the proposed method achieves an
excellent real-time operation speed of 47.07 frames per second with an average
mean dice coefficient score of 0.7786 and mean Intersection over Union of
0.7210 on the out-of-distribution polyp datasets. The results on the publicly
available PolypGen dataset suggest that TransRUPNet can give real-time feedback
while retaining high accuracy for in-distribution datasets. Furthermore, we
demonstrate the generalizability of the proposed method by showing that it
significantly improves performance on out-of-distribution datasets compared to
the existing methods. The source code of our network is available at
https://github.com/DebeshJha/TransRUPNet.
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