Transformer-Enhanced Iterative Feedback Mechanism for Polyp Segmentation Article Swipe

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Nikhil Kumar Tomar , Debesh Jha , Koushik Biswas , Tyler M. Berzin , Rajesh N. Keswani , Michael B. Wallace , Ulaş Bağcı ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2409.05875

Colorectal cancer (CRC) is the third most common cause of cancer diagnosed in the United States and the second leading cause of cancer-related death among both genders. Notably, CRC is the leading cause of cancer in younger men less than 50 years old. Colonoscopy is considered the gold standard for the early diagnosis of CRC. Skills vary significantly among endoscopists, and a high miss rate is reported. Automated polyp segmentation can reduce the missed rates, and timely treatment is possible in the early stage. To address this challenge, we introduce \textit{\textbf{\ac{FANetv2}}}, an advanced encoder-decoder network designed to accurately segment polyps from colonoscopy images. Leveraging an initial input mask generated by Otsu thresholding, FANetv2 iteratively refines its binary segmentation masks through a novel feedback attention mechanism informed by the mask predictions of previous epochs. Additionally, it employs a text-guided approach that integrates essential information about the number (one or many) and size (small, medium, large) of polyps to further enhance its feature representation capabilities. This dual-task approach facilitates accurate polyp segmentation and aids in the auxiliary classification of polyp attributes, significantly boosting the model's performance. Our comprehensive evaluations on the publicly available BKAI-IGH and CVC-ClinicDB datasets demonstrate the superior performance of FANetv2, evidenced by high dice similarity coefficients (DSC) of 0.9186 and 0.9481, along with low Hausdorff distances of 2.83 and 3.19, respectively. The source code for FANetv2 is available at https://github.com/xxxxx/FANetv2.

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Segmentation Computer science Transformer Mechanism (biology) Artificial intelligence Computer vision Engineering Physics Voltage Electrical engineering Quantum mechanics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2409.05875
PDF: https://arxiv.org/pdf/2409.05875
OA Status: green
Cited By: 2
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2409.05875

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Title: Transformer-Enhanced Iterative Feedback Mechanism for Polyp Segmentation

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-08-25

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Authors: Nikhil Kumar Tomar, Debesh Jha, Koushik Biswas, Tyler M. Berzin, Rajesh N. Keswani, Michael B. Wallace, Ulaş Bağcı

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Landing page: https://arxiv.org/abs/2409.05875

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Concepts: Segmentation, Computer science, Transformer, Mechanism (biology), Artificial intelligence, Computer vision, Engineering, Physics, Voltage, Electrical engineering, Quantum mechanics

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Cited by: 2

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