SAU-NET: Scale Aware Polyp Segmentation using Encoder-Decoder Network
Author:
Affiliation:
1. NIT,Computer Science and Engineering,Shillong,Meghalaya,India
2. Nvidia Corporation,Senior Solutions Architect, Deep Learning,Bengaluru,India
Funder
NVIDIA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9864330/9864331/09864338.pdf?arnumber=9864338
Reference22 articles.
1. ResUNet++: An Advanced Architecture for Medical Image Segmentation
2. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs;chen;IEEE Transactions on Pattern Analysis and Machine Intelligence,2017
3. A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution
4. PraNet: Parallel Reverse Attention Network for Polyp Segmentation
5. PolypSegNet: A modified encoder-decoder architecture for automated polyp segmentation from colonoscopy images
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4. Li-SegPNet: Encoder-Decoder Mode Lightweight Segmentation Network for Colorectal Polyps Analysis;IEEE Transactions on Biomedical Engineering;2023-04
5. LPNet: A lightweight CNN with discrete wavelet pooling strategies for colon polyps classification;International Journal of Imaging Systems and Technology;2022-11-14
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