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Found 6 papers, 6 papers with code
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp Detection
Accurate polyp detection is critical for early colorectal cancer diagnosis.
WeakPolyp: You Only Look Bounding Box for Polyp Segmentation
In contrast, polyp bounding box annotations are much cheaper and more accessible.
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).
Ranked #1 on
Abstractive Text Summarization
on CNN/Daily Mail
BoxPolyp:Boost Generalized Polyp Segmentation Using Extra Coarse Bounding Box Annotations
In practice, box annotations are applied to alleviate the over-fitting issue of previous polyp segmentation models, which generate fine-grained polyp area through the iterative boosted segmentation model.
Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-modal Representation Consistency
By aligning the class tokens and spatial attention maps of paired NBI and WL images at different levels, the Transformer achieves the ability to keep both global and local representation consistency for the above two modalities.
Shallow Attention Network for Polyp Segmentation
To address the above issues, we propose the Shallow Attention Network (SANet) for polyp segmentation.
Ranked #9 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
