Search papers, labs, and topics across Lattice.
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.R. Rudolf was supported by DFG grant INST874/9-1.D. Exler (e-mail: david.exler@kit.edu), J. E. Urrutia G贸mez (e-mail: joaquin.urrutia-gomez@kit.edu), M. Kr眉ger (e-mail: martin.krueger2@kit.edu), and M. Reischl (e-mail: markus.reischl@kit.edu) are with the Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.J. Jbeily (e-mail: j.jbeily@doktoranden.th-mannheim.), M. Vitacolonna (e-mail: m.vitacolonna@hs-mannheim.de), and R. Rudolf (e-mail: r.rudolf@hs-mannheim.de) are with the CeMOS Research and Transfer Center, Technische Hochschule Mannheim, Paul-Wittsack-Stra脽e 10, 68163 Mannheim, Germany.M. Schliephake (e-mail: maike.schliephake@student.kit.edu) is with the Institute of Biological and Chemical Systems, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
1
0
3
Automating deep learning pipeline design for 3D biomedical imaging slashes manual annotation effort by intelligently leveraging predicted segmentation instances for assisted classifier training.
