Year: 2024
Type: preprint
Abstract: DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo's unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.
Source: arXiv (Cornell University)
Authors: Patton, William, Rhoades, Jeff L., Zouinkhi, Marwan, Ackerman, David G., Malin-Mayor, Caroline, Adjavon, Diane, Heinrich, Larissa, Bennett, Davis, Zubov, Yurii, Team, CellMap Project, Weigel, Aubrey V., Funke, Jan
Institutions: Howard Hughes Medical Institute, Janelia Research Campus
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403445206