Deep Learning Library for Single Cell Analysis
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Updated
Jun 5, 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
Explainable AI model of cell behavior
Cell cycle inference in single-cell RNA-seq
Tools for building databases of experimental data for constructing whole-cell models
3D shape analysis using deep learning
Automated Cell Toolkit
Rule-based modeling for whole-cell models.
Language for describing whole-cell models as reaction networks
Track single cells and profile the cell cycle with PCNA fluorescence images
Visualise pcnaDeep single-cell tracking data
Simulate a system of linear filaments
Framework for systematically and scalably designing whole-cell models from large datasets
Detecting and Tracking cancer (HeLa) cells using Computer Vision techniques. The project also detects cell division and analyses cell motion such as speed, distance travelled etc. The project uses OpenCV3 for image processing.
Scripts used to create 3D annotations from 2D annotations of cells from fluorescence microscopy images.
A Python package for analyzing XYZ positional data of moving objects over time
Deep Learning Based Cell Segmentation
A general Python framework for using hidden Markov models on binary trees or cell lineage trees.
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