BioinformaticsPLoS ONEPeer-reviewed
Kaizen: Decomposing cellular images with VQ-VAE
Daniel Majoral, Marharyta Domnich
Kaizen improves cell segmentation in microscopy images by using a VQ-VAE to iteratively refine object hypotheses.
2 citations
Author
Daniel Majoral
University of TartuORCID 0000-0002-6037-4747Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2019-2025 · peak 1/yrCo-Author Collaboration Network
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Recent·Most cited
- NeurosciencePLoS Computational BiologyPeer-reviewed
A Model for Time Interval Learning in The Purkinje Cell
Daniel Majoral, Ajmal Zemmar, Raúl Vicente
The paper develops a biophysical model to explain how Purkinje cells learn time intervals through intra-cellular calcium signaling and feedback loops, rather than synaptic weights.
13 citations - BioinformaticsbioRxivPreprint
Segmenting nuclei in brightfield images with neural networks
Dmytro Fishman, Sten‐Oliver Salumaa, Daniel Majoral…·Leopold Parts
This paper demonstrates accurate nucleus segmentation from brightfield images using deep learning, showing that three CNN architectures can achieve this with U-Net performing best.
7 citations