Predicting Protein Content in Grain Using Hyperspectral Deep Learning. - Ole-Christian Galbo Engstrøm; Erik Schou Dreier; Kim Steenstrup Pedersen (ICCVW, 2021)

Improving Deep Learning on Hyperspectral Images of Grain by Incorporating Domain Knowledge from Chemometrics. - Ole-Christian Galbo Engstrøm; Erik Schou Dreier; Birthe Møller Jespersen; Kim Steenstrup Pedersen (ICCV (Workshops), 2023)

Analyzing Near-Infrared Hyperspectral Imaging for Protein Content Regression and Grain Variety Classification Using Bulk References and Varying Grain-to-Background Ratios. - Ole-Christian Galbo Engstrøm; Erik Schou Dreier; Birthe Møller Jespersen; Kim Steenstrup Pedersen (CoRR, 2023)

Challenges in explaining deep learning models for data with biological variation. - Lenka Tetková; Erik Schou Dreier; Robin Malm; Lars Kai Hansen (CoRR, 2024)

IKPLS: Improved Kernel Partial Least Squares and Fast Cross-Validation Algorithms for Python with CPU and GPU Implementations Using NumPy and JAX. - Ole-Christian Galbo Engstrøm; Erik Schou Dreier; Birthe Møller Jespersen; Kim Steenstrup Pedersen (J. Open Source Softw., 2024)

Transforming Hyperspectral Images Into Chemical Maps: An End-to-End Deep Learning Approach. - Ole-Christian Galbo Engstrøm; Michela Albano-Gaglio; Erik Schou Dreier; Yamine Bouzembrak; Maria Font-i-Furnols; Puneet Mishra; Kim Steenstrup Pedersen (CoRR, 2025)

A Time Series Dataset of NIR Spectra and RGB and NIR-HSI Images of the Barley Germination Process. - Ole-Christian Galbo Engstrøm; Erik Schou Dreier; Birthe Møller Jespersen; Kim Steenstrup Pedersen (CoRR, 2025)