DLSIA: Deep Learning for Scientific Image Analysis

TL;DR

DLSIA is a Python library that simplifies the application of customizable deep learning CNN architectures for scientific image analysis, enabling researchers to efficiently process complex data across disciplines.

Contribution

The paper introduces DLSIA, a versatile Python library with novel sparse mixed-scale networks and user-friendly CNN architectures tailored for scientific image analysis.

Findings

Provides accessible CNN tools for scientists

Introduces sparse mixed-scale networks (SMSNets)

Facilitates interdisciplinary research and discovery

Abstract

We introduce DLSIA (Deep Learning for Scientific Image Analysis), 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, or for experiment-in-the-loop computing scenarios. DLSIA features easy-to-use architectures such as autoencoders, tunable U-Nets, and parameter-lean mixed-scale dense networks (MSDNets). Additionally, we introduce sparse mixed-scale networks (SMSNets), generated using random graphs and sparse connections. As experimental data continues 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.