Weakly-supervised retinal detachment segmentation using deep feature propagation learning in SD-OCT images
In this paper, we propose a weakly supervised learning method for the quantitative analysis of lesion regions in spectral domain optical coherence tomography (SD-OCT) images. The experimental results demonstrate that the proposed method can achieve encouraging segmentation accuracy comparable to strong supervision methods only utilizing image-level labels.
Quantitative Estimation of Rainfall Rate Intensity Based on Deep Convolutional Neural Network and Radar Reflectivity Factor
We propose a method based on the deep convolutional neural network model VGG and radar reflectivity factor to quantitatively estimation the rainfall rate intensity, which eclipses the performance of the traditional method Z-R relationship.
