pathml

Whole-slide image analysis for computational pathology involves loading image files, preprocessing, segmenting nuclei, and training machine learning models. This toolkit supports 160+ formats and includes pre-trained models for nucleus detection and classification.

Yes, machine learning for nucleus detection and segmentation on pathology slides is fully supported. The toolkit provides pre-trained models for immediate deployment and allows you to train custom classification models on your whole-slide image datasets.

Whole-slide imaging preprocessing supports over 160 different image formats. This broad compatibility ensures you can load and process diverse pathology slide files directly within your computational pathology workflow without prior format conversion.

Spatial analysis of cellular relationships in tissue images is done by constructing and analyzing spatial graphs. This allows you to quantify cellular and tissue-level interactions directly from your segmented whole-slide imaging data.

This computational pathology toolkit is suitable for clinical, research, and educational settings. It provides an end-to-end workflow for loading, preprocessing, and analyzing whole-slide images using machine learning and spatial graph construction.

The best way to start whole-slide image processing in Python is installing the pathml dependency and loading a slide file. You can then immediately apply preprocessing, segmentation, and machine learning pipelines to your pathology data.