176 citations · 236 across the 4 of their papers we have counts for
5 papers
Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa
John Lagergren, Mirko Pavicic, Hari B. Chhetri +10
Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the…
Region Growing with Convolutional Neural Networks for Biomedical Image Segmentation
John Lagergren, Erica Rutter, Kevin Flores
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. T…
Biologically-informed neural networks guide mechanistic modeling from sparse experimental data
John H. Lagergren, John T. Nardini, Ruth E. Baker +2
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological syste…
Learning Equations from Biological Data with Limited Time Samples
John T. Nardini, John H. Lagergren, Andrea Hawkins-Daarud +5
Equation learning methods present a promising tool to aid scientists in the modeling process for biological data. Previous equation learning studies have demonstrated that these me…
Learning partial differential equations for biological transport models from noisy spatiotemporal data
John Lagergren, John T. Nardini, G. Michael Lavigne +2
We investigate methods for learning partial differential equation (PDE) models from spatiotemporal data under biologically realistic levels and forms of noise. Recent progress in l…