pathml

You can process whole-slide images for computational pathology by loading raw WSI files, applying stain normalization, segmenting nuclei, and extracting quantitative features from image tiles to build end-to-end digital pathology pipelines.

Yes, you can normalize H&E staining variations and perform nuclear segmentation on whole-slide images. The workflow harmonizes staining across slides and detects tissue regions to prepare data for quantitative analysis.

The best way to extract quantitative features from digital pathology slides is to tile whole-slide images into patches, apply preprocessing steps like stain normalization, and extract features from the resulting image tiles for machine learning models.

Yes, the whole-slide imaging workflow supports both H&E stained slides and multiplex imaging. It enables preprocessing, tiling, and feature extraction across different staining protocols for computational pathology pipelines.

To tile whole-slide images into patches for machine learning, you load the WSI file and divide it into standardized patch sizes, such as 256x256, while applying tissue detection to filter relevant regions for downstream analysis.

Yes, PyTorch is required as a dependency to run nuclei segmentation and feature extraction on whole-slide images. The pipeline integrates with Python ML libraries like Torch and Torchvision for building digital pathology workflows.