Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation
Quang Vinh Nguyen (Chonnam National University),* Van Thong Huynh (Chonnam National University), Soo-Hyung Kim (Chonnam National University)
The 34th British Machine Vision Conference
Abstract
Colonoscopy is a common and practical method for detecting and treating polyps. Segmenting polyps from colonoscopy image is useful for diagnosis and surgery progress. Nevertheless, achieving excellent segmentation performance is still difficult because of polyp characteristics like shape, color, condition, and obvious non-distinction from the surrounding context. This work presents a new novel architecture namely Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation (ADSNet), which modifies misclassified details and recovers weak features having the ability to vanish and not be detected at the final stage. The architecture consists of a complementary trilateral decoder to produce an early global map. A continuous attention module modifies semantics of high-level features to analyze two separate semantics of the early global map. The suggested method is experienced on polyp benchmarks in learning ability and generalization ability, experimental results demonstrate the great correction and recovery ability leading to better segmentation performance compared to the other state of the art in the polyp image segmentation task. Especially, the proposed architecture could be experimented flexibly for other CNN-based encoders, Transformer-based encoders, and decoder backbones.Video
Citation
@inproceedings{Nguyen_2023_BMVC,
author = {Quang Vinh Nguyen and Van Thong Huynh and Soo-Hyung Kim},
title = {Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0806.pdf}
}
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