Tomar, NK, Jha, D, Bagci, U et al. (1 more author) (2022) TGANet: Text-Guided Attention for Improved Polyp Segmentation. In: Wang, L, Dou, Q, Fletcher, PT, Speidel, S and Li, S, (eds.) Lecture Notes in Computer Science. Springer Nature Switzerland , Cham, Switzerland , pp. 151-160. ISBN 9783031164361
Abstract
Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps. In this work, we exploit size-related and polyp number-related features in the form of text attention during training. We introduce an auxiliary classification task to weight the text-based embedding that allows network to learn additional feature representations that can distinctly adapt to differently sized polyps and can adapt to cases with multiple polyps. Our experimental results demonstrate that these added text embeddings improve the overall performance of the model compared to state-of-the-art segmentation methods. We explore four different datasets and provide insights for size-specific improvements. Our proposed text-guided attention network (TGANet) can generalize well to variable-sized polyps in different datasets. Codes are available at https://github.com/nikhilroxtomar/TGANet.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author produced version of a conference paper published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Label embedding; Polyp; Multi-scale features; Attention |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Oct 2022 14:39 |
Last Modified: | 16 Sep 2023 00:13 |
Status: | Published |
Publisher: | Springer Nature Switzerland |
Identification Number: | 10.1007/978-3-031-16437-8_15 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191889 |