Keep It Accurate and Robust: An Enhanced Nuclei Analysis Framework
This work addresses a critical problem in pathology for medical researchers and clinicians by providing a robust nuclei analysis tool, though it is incremental as it builds on existing methods like HoVer-Net.
The paper tackles the challenge of accurate nuclei segmentation and classification in histology images by proposing an enhanced framework with dual-level ensemble modeling and HoVer-Net improvements, achieving state-of-the-art results including 1st place in nuclear composition prediction and 3rd place in segmentation/classification on the CoNIC Challenge.
Accurate segmentation and classification of nuclei in histology images is critical but challenging due to nuclei heterogeneity, staining variations, and tissue complexity. Existing methods often struggle with limited dataset variability, with patches extracted from similar whole slide images (WSI), making models prone to falling into local optima. Here we propose a new framework to address this limitation and enable robust nuclear analysis. Our method leverages dual-level ensemble modeling to overcome issues stemming from limited dataset variation. Intra-ensembling applies diverse transformations to individual samples, while inter-ensembling combines networks of different scales. We also introduce enhancements to the HoVer-Net architecture, including updated encoders, nested dense decoding and model regularization strategy. We achieve state-of-the-art results on public benchmarks, including 1st place for nuclear composition prediction and 3rd place for segmentation/classification in the 2022 Colon Nuclei Identification and Counting (CoNIC) Challenge. This success validates our approach for accurate histological nuclei analysis. Extensive experiments and ablation studies provide insights into optimal network design choices and training techniques. In conclusion, this work proposes an improved framework advancing the state-of-the-art in nuclei analysis. We release our code and models (https://github.com/WinnieLaugh/CONIC_Pathology_AI) to serve as a toolkit for the community.