Tissue Aware Nuclei Detection and Classification Model for Histopathology Images

• URL: http://arxiv.org/abs/2511.13615v1
• Date: Mon, 17 Nov 2025 17:21:05 GMT
• Title: Tissue Aware Nuclei Detection and Classification Model for Histopathology Images
• Authors: Kesi Xu, Eleni Chiou, Ali Varamesh, Laura Acqualagna, Nasir Rajpoot,
• Abstract summary: We present Tissue-Aware Nuclei Detection (TAND), a novel framework achieving joint nuclei detection and classification.<n>TAND couples a ConvNeXt-based encoder-decoder with a frozen Virchow-2 tissue segmentation branch, where semantic tissue probabilities selectively modulate the classification stream.<n>TAND achieves state-of-the-art performance, surpassing both tissue-agnostic baselines and mask-supervised methods.
• Score: 0.5219568203653522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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