Editing Pathology Ai (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Building a pathology AI pipeline:
# Data: collect WSIs with slide-level labels (diagnosis, grade, biomarker status) from pathology archive.
# Preprocessing: tissue segmentation, patch extraction at 256×256 / 20×, feature extraction with UNI or CONCH.
# MIL training: CLAM with 5-fold cross-validation; attention-based pooling.
# Interpretability: generate attention heatmaps overlaid on WSI; pathologist verification.
# Bias audit: evaluate performance across patient demographics.
# Clinical validation: prospective reader study at target institution.
# Regulatory: work with regulatory consultant on FDA 510(k) or De Novo pathway.

[[Category:Artificial Intelligence]]
[[Category:Pathology]]
[[Category:Medical Imaging]]
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