DLSIA: Deep Learning for Scientific Image Analysis

Roberts, Eric J.; Chavez, Tanny; Hexemer, Alexander; Zwart, Petrus H.

doi:10.1107/S1600576724001390

Title: DLSIA: Deep Learning for Scientific Image Analysis

Journal Article · 20 March 2024 · Journal of Applied Crystallography (Online)

DOI: https://doi.org/10.1107/S1600576724001390 · OSTI ID:2328638

Roberts, Eric J.; Chavez, Tanny;

;

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NONE; AC02-05CH11231

OSTI ID:: 2328638

Alternate ID(s):: OSTI ID: 2448508

Journal Information:: Journal of Applied Crystallography (Online), Journal Name: Journal of Applied Crystallography (Online) Journal Issue: 2 Vol. 57; ISSN 1600-5767; ISSN JACGAR

Publisher:: International Union of Crystallography (IUCr)Copyright Statement

Country of Publication:: Denmark

Language:: English

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