Optimizing Lymphocyte Detection in Breast Cancer Whole Slide Imaging through Data-Centric Strategies

TL;DR

This paper presents a data-centric optimization pipeline that significantly improves lymphocyte detection in breast cancer histopathology slides using standard models and novel dataset augmentation strategies, enabling high-performance biomarker development.

Contribution

The study introduces novel biological upsampling and visual transformations tailored for tissue imagery, demonstrating that standard models can achieve high accuracy with strategic data augmentation.

Findings

Enhanced lymphocyte detection accuracy in breast cancer slides

Effective dataset augmentation strategies improve model performance

Standard models can be highly effective with proper data curation

Abstract

Efficient and precise quantification of lymphocytes in histopathology slides is imperative for the characterization of the tumor microenvironment and immunotherapy response insights. We developed a data-centric optimization pipeline that attain great lymphocyte detection performance using an off-the-shelf YOLOv5 model, without any architectural modifications. Our contribution that rely on strategic dataset augmentation strategies, includes novel biological upsampling and custom visual cohesion transformations tailored to the unique properties of tissue imagery, and enables to dramatically improve model performances. Our optimization reveals a pivotal realization: given intensive customization, standard computational pathology models can achieve high-capability biomarker development, without increasing the architectural complexity. We showcase the interest of this approach in the context of breast cancer where our strategies lead to good lymphocyte detection performances, echoing a broadly impactful paradigm shift. Furthermore, our data curation techniques enable crucial histological analysis benchmarks, highlighting improved generalizable potential.