pathml

Whole-slide pathology image processing for machine learning involves applying modular pipelines for quality control, stain normalization, and tissue detection before storing the prepared datasets in HDF5 format for training models.

Computational pathology analysis supports over 160 proprietary and standard image formats, utilizing backend libraries like openslide and bioformats to load whole-slide images for subsequent preprocessing and machine learning workflows.

Yes, this Skill analyzes multiparametric imaging data including CODEX, Vectra, and MERFISH formats, enabling researchers to process complex spatial and multiplexed pathology datasets alongside standard whole-slide images.

Yes, PyTorch is required as a backend dependency to run pre-trained machine learning models for nucleus detection and segmentation, supporting computational pathology workflows with deep learning capabilities.

Manage large pathology datasets during image analysis by utilizing HDF5 via the h5py dependency, which efficiently stores and handles large whole-slide imaging arrays generated during preprocessing and machine learning workflows.

Build spatial graphs for cellular relationships in pathology images by applying the graph construction features after nucleus detection and segmentation, mapping spatial relationships between cells and tissues for downstream analysis.