Repository Index

ZeroCostDL4Mic: A Google Colab based no-cost toolbox to explore Deep-Learning in Microscopy

Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.

• histopathology-images
• microscopy
• sliding-windows
• medical-image-analysis

Tools for computational pathology

• machine-learning
• digital-pathology
• computational-pathology
• biomedical-image-processing

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

• ai
• cell
• digitalpathology
• microscopy

Automated 3D cell detection in very large images

• microscopy
• cell-detection
• deep-learning
• image-analysis

Deep and Machine Learning for Microscopy

• microscopy
• materials
• machine-learning
• materials-science

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

• tensorflow
• python3
• keras
• resnet-101

PyTorch Connectomics: segmentation toolbox for EM connectomics

• computer-vision
• neuroscience
• connectomics
• pytorch

Collection of tools and scripts useful to automate microscopy workflows in ZEN Blue using Python and Open Application Development tools and AI tools.

• python
• microscopy
• scripting
• automation

Axon/Myelin segmentation using Deep Learning

• deep-learning
• machine-learning
• myelin
• axon

Nellie: Automated organelle segmentation, tracking, and hierarchical feature extraction in 2D/3D live-cell microscopy

• biology
• fluorescence
• image-analysis
• image-processing

A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources

• bioimage-analysis
• cell-classification
• computer-vision
• digital-pathology

Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.

• microscopy
• image-processing
• segmentation
• biology

DetecDiv provides a comprehensive set of tools to analyze time microscopy images using deep learning methods. The software structure is such that data can be processed either at the command line or using a graphical user-interface. Detecdiv classification models include : image classification and regression, semantic segmentation, LSTM networks to analyze data and image timeseries.

Panoptic segmentation algorithms for 2D and 3D electron microscopy images

• deep-learning
• semantic-segmentation
• segmentation
• pytorch