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Histopathology Image Analysis
Resources for teaching/preparing to teach bioimage analysis
ImageJ plugins written to help develop https://bioimagebook.github.io
Images & code to visualize to illustrate the challenges image analysis in pathology
Bioimage analysis pipelines to detect cells (nucleus + cytoplasm) and generate label images with the aim of establishing an object hierarchy
Digital pathology image viewer with support for human/machine generated annotations and markups.
An open-source, web-based viewer for zoomable images, implemented in pure JavaScript.
Aydin — User-friendly, Fast, Self-Supervised Image Denoising for All.
Python Jupyter notebooks for BioImageAnalysis, GPU-accelerated image processing, bio-image data science and more
The ImageJ plugin to run deep-learning models
Intelligent data partitioning using quality control metrics
TissUUmaps is a browser-based tool for fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used as a web service or locally in your computer,…
Use this to download all elements of the BCSS dataset described in: Amgad M, Elfandy H, ..., Gutman DA, Cooper LAD. Structured crowdsourcing enables convolutional segmentation of histology images. …
A fast image processing library with low memory needs.
MarrowQuant is a user-friendly algorithm for the quantification of H&E bone marrow tissue biopsies in whole slide images, implemented as a series of QuPath Scripts. Keywords: Digital Pathology, Who…
A pan-cancer platform for mutation prediction from routine histology
BIRL: Benchmark on Image Registration methods with Landmark validations
Following repository demonstrates machine learning architectures that can correctly classify lesions between LM and AMH. Overall, our methods showcase the potential for computer-aided diagnosis in …
Tutorial to learn how to implement transfer learning in histopathology with Tensorflow 2.0
Full package for applying deep learning to virtual slides.
The PatchCamelyon (PCam) deep learning classification benchmark.
Full package for applying deep learning to virtual slides.
Computational Pathology Toolbox developed by TIA Centre, University of Warwick.
A standardized Python API with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology.
Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay