Perisomatic Features Enable Efficient and Dataset Wide Cell-Type Classifications Across Large-Scale Electron Microscopy Volumes
Elabbady, Leila, Seshamani, Sharmishtaa, Shang Mu, Mahalingam, Gayathri, Schneider-Mizell, Casey M, Bodor, Agnes, Bae, J Alexander, Brittain, Derrick, Buchanan, Joann, Bumbarger, Daniel J, Castro, Manuel A, Dorkenwald, Sven, Halageri, Akhilesh, Jia, Zhen, Jordan, Chris, Kapner, Dan, Kemnitz, Nico, Kinn, Sam, Lee, Kisuk, Li, Kai, Lu, Ran, Macrina, Thomas, Mitchell, Eric, Mondal, Shanka Subhra, Barak Nehoran, Popovych, Sergiy, Silversmith, William, Takeno, Marc, Torres, Russel, Turner, Nicholas L, Wong, William, Wu, Jingpeng, Yin, Wenjing, Yu, Szi-Chieh, The Microns Consortium, Seung, H Sebastian, Reid, R Clay, Nuno Macarico Da Costa, Collman, rest
Published in bioRxiv (13.01.2024)
Published in bioRxiv (13.01.2024)
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