a generalist algorithm for cellular segmentation with human-in-the-loop capabilities

Deep Learning Library for Single Cell Analysis

Usiigaci: stain-free cell tracking in phase contrast microscopy enabled by supervised machine learning

Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22, CVPR'22, MICCAI'23, Histopathology'23]

(NeurIPS 2022 CellSeg Challenge - 1st Winner) Open source code for "MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy"

CellO: Gene expression-based hierarchical cell type classification using the Cell Ontology

Count-Ception: Counting by Fully Convolutional Redundant Counting

Integrated Cell project implemented in pytorch

Multiple-particle tracking designed to (1) track dense particle fields, (2) close gaps in particle trajectories resulting from detection failure, and (3) capture particle merging and splitting events resulting from occlusion or genuine aggregation and dissociation events

Cell cycle inference in single-cell RNA-seq

Interactive deep learning whole-cell segmentation and thresholding using partial annotations

Detect morphological motifs, such as blebs, filopodia, and lamellipodia, from 3D images of surfaces, particularly images of cell surfaces.

Multiple particle tracking in dense 3D particle fields complemented with dynamic regions of interest and trackability inferences for the automated exploration of large volumetric sequences.

Automated Cell Toolkit

Ensemble Learning for Harmonization and Annotation of Single Cells (ELeFHAnt) provides an easy to use R package for users to annotate clusters of single cells, harmonize labels across single cell datasets to generate a unified atlas and infer relationship among celltypes between two datasets. It uses an ensemble of three machine learning classif…

Communitiy-based Cell Line Ontology

3D shape analysis using deep learning

Explainable AI model of cell behavior

Transform 3D cell surfaces into different representations including topographic maps, 3D spheres, and 2D images for doing optimized quantification, data analysis and machine learning.

genomics, biochemistry, biotechnology, cell biology, biophysics, ecology