DLSIA: Deep Learning for Scientific Image Analysis

• URL: http://arxiv.org/abs/2308.02559v2
• Date: Sat, 26 Aug 2023 18:03:39 GMT
• Title: DLSIA: Deep Learning for Scientific Image Analysis
• Authors: Eric J Roberts, Tanny Chavez, Alexander Hexemer, Petrus H. Zwart
• Abstract summary: DLSIA 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. DLSIA features easy-to-use architectures such as autoencoders, tunable U-Nets, and parameter-lean mixed-scale dense networks (MSDNets)
• Score: 45.81637398863868
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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