DeNuC: Decoupling Nuclei Detection and Classification in Histopathology

TL;DR

DeNuC introduces a decoupled approach for nuclei detection and classification in histopathology, significantly improving performance and efficiency over existing methods by addressing the limitations of joint optimization in foundation models.

Contribution

The paper proposes DeNuC, a novel method that decouples nuclei detection and classification, enhancing representation and accuracy in pathology image analysis.

Findings

DeNuC outperforms state-of-the-art methods on three benchmarks.

F1 scores improve by over 4% on key datasets.

Uses only 16% of trainable parameters compared to existing approaches.

Abstract

Pathology Foundation Models (FMs) have shown strong performance across a wide range of pathology image representation and diagnostic tasks. However, FMs do not exhibit the expected performance advantage over traditional specialized models in Nuclei Detection and Classification (NDC). In this work, we reveal that jointly optimizing nuclei detection and classification leads to severe representation degradation in FMs. Moreover, we identify that the substantial intrinsic disparity in task difficulty between nuclei detection and nuclei classification renders joint NDC optimization unnecessarily computationally burdensome for the detection stage. To address these challenges, we propose DeNuC, a simple yet effective method designed to break through existing bottlenecks by Decoupling Nuclei detection and Classification. DeNuC employs a lightweight model for accurate nuclei localization, subsequently leveraging a pathology FM to encode input images and query nucleus-specific features based on the detected coordinates for classification. Extensive experiments on three widely used benchmarks demonstrate that DeNuC effectively unlocks the representational potential of FMs for NDC and significantly outperforms state-of-the-art methods. Notably, DeNuC improves F1 scores by 4.2% and 3.6% (or higher) on the BRCAM2C and PUMA datasets, respectively, while using only 16% (or fewer) trainable parameters compared to other methods. Code is available at https://github.com/ZijiangY1116/DeNuC.