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Found 7 papers, 1 papers with code
Accurate Real-time Polyp Detection in Videos from Concatenation of Latent Features Extracted from Consecutive Frames
We propose an efficient feature concatenation method for a CNN-based encoder-decoder model without adding complexity to the model.
Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polyp
In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones.
Confidence-Aware Learning for Deep Neural Networks
Despite the power of deep neural networks for a wide range of tasks, an overconfident prediction issue has limited their practical use in many safety-critical applications.
Bin-wise Temperature Scaling (BTS): Improvement in Confidence Calibration Performance through Simple Scaling Techniques
The prediction reliability of neural networks is important in many applications.
Polyp Detection and Segmentation using Mask R-CNN: Does a Deeper Feature Extractor CNN Always Perform Better?
Automatic polyp detection and segmentation are highly desirable for colon screening due to polyp miss rate by physicians during colonoscopy, which is about 25%.
Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches
Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy.
Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance
In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images.
