DeNuC: Decoupling Nuclei Detection and Classification in Histopathology

Context

However, FMs do not exhibit the expected performance advantage over traditional specialized models in Nuclei Detection and Classification (NDC). In this work, Hugging Face Daily Papers reveal that jointly optimizing nuclei detection and classification leads to severe representation degradation in FMs. Moreover, Hugging Face Daily Papers 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, Hugging Face Daily Papers proposes 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…

For builders

However, FMs do not exhibit the expected performance advantage over traditional specialized models in Nuclei Detection and Classification (NDC).