Tissue Aware Nuclei Detection and Classification Model for Histopathology Images

TL;DR

This paper introduces TAND, a tissue-aware framework for nuclei detection and classification in histopathology images that leverages tissue context to improve accuracy with minimal supervision.

Contribution

TAND is the first method to incorporate tissue mask conditioning into nuclei classification, reducing annotation needs and improving tissue-dependent cell type detection.

Findings

Achieves state-of-the-art performance on PUMA benchmark.

Significantly improves detection of tissue-dependent cell types.

Outperforms tissue-agnostic and mask-supervised baselines.

Abstract

Accurate nuclei detection and classification are fundamental to computational pathology, yet existing approaches are hindered by reliance on detailed expert annotations and insufficient use of tissue context. We present Tissue-Aware Nuclei Detection (TAND), a novel framework achieving joint nuclei detection and classification using point-level supervision enhanced by tissue mask conditioning. TAND couples a ConvNeXt-based encoder-decoder with a frozen Virchow-2 tissue segmentation branch, where semantic tissue probabilities selectively modulate the classification stream through a novel multi-scale Spatial Feature-wise Linear Modulation (Spatial-FiLM). On the PUMA benchmark, TAND achieves state-of-the-art performance, surpassing both tissue-agnostic baselines and mask-supervised methods. Notably, our approach demonstrates remarkable improvements in tissue-dependent cell types such as epithelium, endothelium, and stroma. To the best of our knowledge, this is the first method to condition per-cell classification on learned tissue masks, offering a practical pathway to reduce annotation burden.