تقرير
LAPFormer: A Light and Accurate Polyp Segmentation Transformer
| العنوان: | LAPFormer: A Light and Accurate Polyp Segmentation Transformer |
|---|---|
| المؤلفون: | Nguyen, Mai, Bui, Tung Thanh, Van Nguyen, Quan, Nguyen, Thanh Tung, Van Pham, Toan |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Polyp segmentation is still known as a difficult problem due to the large variety of polyp shapes, scanning and labeling modalities. This prevents deep learning model to generalize well on unseen data. However, Transformer-based approach recently has achieved some remarkable results on performance with the ability of extracting global context better than CNN-based architecture and yet lead to better generalization. To leverage this strength of Transformer, we propose a new model with encoder-decoder architecture named LAPFormer, which uses a hierarchical Transformer encoder to better extract global feature and combine with our novel CNN (Convolutional Neural Network) decoder for capturing local appearance of the polyps. Our proposed decoder contains a progressive feature fusion module designed for fusing feature from upper scales and lower scales and enable multi-scale features to be more correlative. Besides, we also use feature refinement module and feature selection module for processing feature. We test our model on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib Comment: 7 pages, 7 figures, ACL 2023 underreview |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2210.04393 |
| رقم الأكسشن: | edsarx.2210.04393 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |