Prioritizing cases from a multi-institutional cohort for a dataset of pathologist annotations.

ABSTRACT:

Objective

With the increasing energy surrounding the development of artificial intelligence and machine learning (AI/ML) models, the use of the same external validation dataset by various developers allows for a direct comparison of model performance. Through our High Throughput Truthing project, we are creating a validation dataset for AI/ML models trained in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple negative breast cancer (TNBC).

Materials and methods

We obtained clinical metadata for hematoxylin and eosin-stained glass slides and corresponding scanned whole slide images (WSIs) of TNBC core biopsies from two US academic medical centers. We selected regions of interest (ROIs) from the WSIs to target regions with various tissue morphologies a

SUBMITTER: Garcia V 

PROVIDER: S-EPMC11667696 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature