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Found 7 papers, 3 papers with code
Multi-Layer Dense Attention Decoder for Polyp Segmentation
Detecting and segmenting polyps is crucial for expediting the diagnosis of colon cancer.
Aggregating Global Features into Local Vision Transformer
Local Transformer-based classification models have recently achieved promising results with relatively low computational costs.
A Discriminative Channel Diversification Network for Image Classification
Channel attention mechanisms in convolutional neural networks have been proven to be effective in various computer vision tasks.
Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation
Colonoscopy is a procedure to detect colorectal polyps which are the primary cause for developing colorectal cancer.
Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations
This work can serve as a baseline for future research in polyp detection and classification.
Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks
The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences.
A Comparative Study on Polyp Classification using Convolutional Neural Networks
In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification.
