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Found 3 papers, 2 papers with code
Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
In this way, the current work is designed to provide the plant phenotyping community with (i) methods for fast and accurate image-based feature extraction that require minimal training data, and (ii) a new population-scale data set, including 68 different leaf phenotypes, for domain scientists and machine learning researchers.
Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation
In this paper we present a methodology that uses convolutional neural networks (CNNs) for segmentation by iteratively growing predicted mask regions in each coordinate direction.
Learning partial differential equations for biological transport models from noisy spatiotemporal data
We analyze the performance in utilizing previous methods to denoise data for the task of discovering the governing system of partial differential equations (PDEs).
Dynamical Systems
