Deep Learning Library for Single Cell Analysis
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Updated
Aug 30, 2024 - Python
Deep Learning Library for Single Cell Analysis
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23, MICCAI'24]
(NeurIPS 2022 CellSeg Challenge - 1st Winner) Open source code for "MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy"
Integrated Cell project implemented in pytorch
Cell cycle inference in single-cell RNA-seq
Automated Cell Toolkit
3D shape analysis using deep learning
Explainable AI model of cell behavior
genomics, biochemistry, biotechnology, cell biology, biophysics, ecology
Track single cells and profile the cell cycle with PCNA fluorescence images
Rule-based modeling for whole-cell models.
A general Python framework for using hidden Markov models on binary trees or cell lineage trees.
Language for describing whole-cell models as reaction networks
A Python package for analyzing XYZ positional data of moving objects over time
Quantifying 2D cell shape and epithelial tissue dynamics
Framework for systematically and scalably designing whole-cell models from large datasets
Deep Learning Based Cell Segmentation
A simple plugin to detect vesicles in cells images.
Tools for building databases of experimental data for constructing whole-cell models
Fluospotter is an end-to-end pipeline designed for nuclei segmentation and puncta detection in fluorescence microscopy images.
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