Jul 2026· International Conference on Signal Processing and Communications· pp. 1-5· 0 citations· 41 references
Abstract
Accurate colonoscopic polyp segmentation is essential for early colorectal cancer detection, yet remains challenging due to low contrast, specular highlights, blurred boundaries, and large variations in polyp appearance. We propose DFANet, a dual-branch feature aggregation framework that combines transformer-driven global semantic modeling MiT-B5 with CNN-based fine-grained representation learning EfficientNet-B5. A channel-spatial fusion module adaptively integrates multi-scale encoder outputs through element-wise aggregation, channel attention, and spatial attention, followed by a DSC Transformer that enhances contextual reasoning before decoding. The multi-stage decoder progressively restores resolution while refining structural details to produce accurate segmentation masks. Extensive experiments on five benchmark datasets demonstrate consistent performance gains, with DFANet achieving improvements of up to 3-7% in Dice and mIoU over state-of-the-art methods on Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, ETIS-LaribPolypDB, and CVC-ColonDB.
Accurate polyp segmentation in colonoscopy images is critical for early detection and prevention of colorectal cancer, yet remains challenging due to variable polyp appearance, size, and boundary ambiguity. While recent transformer-based encoders such as Mix Vision Transformer (MiT) have demonstrated strong feature ext...
The Staged Global-to-Local Cross-Scale Fusion Network (SGLF-Net), which adopts a novel staged global-to-local learning paradigm to progressively refine segmentation from coarse global semantics to fine-grained local details, is proposed.
Tan Guo, Wen-Han Zhang, Fu-Lin Luo et al.· IEEE journal of biomedical a...· 0 citations
Accurate segmentation of colorectal polyps in colonoscopy images is crucial for the early detection and prevention of colorectal cancer (CRC). However, polyp segmentation remains challenging due to variations in size, shape, texture, and boundary ambiguity caused by specular highlights, motion blur, and low contrast. I...
Manoj Kumar Singh, Satish Chand, K. Sahu· International journal of ima...· 0 citations
NAHFormer, a medical image segmentation framework equipped with boundary-aware and redundancy-aware capabilities, is proposed, which consistently outperforms state-of-the-art methods on colonoscopy polyp and dermoscopy datasets.
Accurate segmentation of colorectal polyps can support computer-assisted analysis of colonoscopic images, but variation in polyp morphology, low lesion-to-background contrast, and indistinct boundaries remain challenging. We propose DCD-DeepLabv3+, an enhanced DeepLabv3+-based model designed to improve polyp segmentati...
Junpei Zhang· 计算机科学辑要· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
