Computational Pathology Toolbox developed by TIA Centre, University of Warwick.

Iterative transfer learning with neural network improves clustering and cell type classification in single-cell RNA-seq analysis

Large-Scale Multi-Class Image-Based Cell Classification with Deep Learning

Python package for analyzing ephys data

Predict which cell is cancerous with 96% accuracy using SVM machine learning algorithm.

Interactive modules to digitally annotate H&E whole slide images, construct cell and tissue level features and classify cell types. Specifically for a case study of differentiating borderline and high-grade serous ovarian tumors.

Cell image analysis pipeline for RPE cell identification, counting and maturity classification

Label Free Identification of Different Cancer Cells Using Deep Learning-Based Image Analysis

Project on Neural Networks for Nanoparticle Breast Cell Classification - Machine Learning (MEng), supervised by Prof. C. Sansone and Eng. M. Gravina(2024)

Non invasive live cell cycle monitoring using a supervised deep neural autoencoder onquantitative phase images

Notes about single cell technology and computational analysis methods, including but not limitted to Genome, Epigenome, Transcriptome, Morphology, Connectome and the integrative analysis of information from these layers.

Computer vision single cell Tradescantia clone 4430 set of 204 images.

Inference code for GrEp : Graph-based epithelial cell sub-classification refinement

Project for the Applied Machine Learning (EI70360) class at TUM Summer Semester 2023.