PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection

Background:
Deep learning models for patch classification in whole-slide images (WSIs) have shown promise in assisting follicular lymphoma grading. However, these models often require pathologists to identify centroblasts and manually provide refined labels for model optimization.
Objective:
To address this limitation, we propose
PseudoCell
, an object detection framework for automated centroblast detection in WSI, eliminating the need for extensive pathologist's refined labels.
Methods:

PseudoCell
leverages a combination of pathologist-provided centroblast labels and pseudo-negative labels generated from undersampled false-positive predictions based on cell morphology features. This approach reduces the reliance on time-consuming manual annotations.
Results:
Our framework significantly reduces the workload for pathologists by accurately identifying and narrowing down areas of interest containing centroblasts. Depending on the confidence threshold,
PseudoCell
can eliminate 58.18-99.35% of irrelevant tissue areas on WSI, streamlining the diagnostic process.
Conclusion:
This study presents
PseudoCell
as a practical and efficient prescreening method for centroblast detection, eliminating the need for refined labels from pathologists. The discussion section provides detailed guidance for implementing
PseudoCell
in clinical practice.