Li-SegPNet: Encoder-Decoder Mode Lightweight Segmentation Network for Colorectal Polyps Analysis-Reference-Cited by-同舟云学术

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Li-SegPNet: Encoder-Decoder Mode Lightweight Segmentation Network for Colorectal Polyps Analysis

Published:2023-04 Issue:4 Volume:70 Page:1330-1339
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Sharma Pallabi¹^ORCID,Gautam Anmol²,Maji Pallab³,Pachori Ram Bilas⁴^ORCID,Balabantaray Bunil Kumar²

Affiliation:

1. Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, India

2. Department of Computer Science and Engineering, National Institute of Technology Meghalaya, India

3. NVIDIA Graphics Pvt. Ltd., India

4. Department of Electrical Engineering, Indian Institute of Technology Indore, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/10078065/09926143.pdf?arnumber=9926143

Reference44 articles.

1. Deep Residual Learning for Image Recognition

2. UNETR: Transformers for 3D Medical Image Segmentation

3. Road Extraction by Deep Residual U-Net

4. A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation

5. WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians

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