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Found 6 papers, 1 papers with code
UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection
Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy.
Deep Learning-based Aerial Image Segmentation with Open Data for Disaster Impact Assessment
Experimental results show that pretraining on ImageNet usually improves the segmentation performance for a number of models.
CNN-Based Semantic Change Detection in Satellite Imagery
Graph theory is combined with the CNN output for detecting semantic changes in road networks with OpenStreetMap data.
3D Point Cloud Feature Explanations Using Gradient-Based Methods
Driven by the insight that 3D data is inherently sparse, we visualise the features learnt by a voxel-based classification network and show that these features are also sparse and can be pruned relatively easily, leading to more efficient neural networks.
Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks
The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an F_score of 82. 1% on the ISPRS benchmark dataset, comparable to the state-of-the-art methods but with increased efficiency.
Multi-Temporal Aerial Image Registration Using Semantic Features
A semantic feature extraction method for multitemporal high resolution aerial image registration is proposed in this paper.
