Tissue Aware Nuclei Detection and Classification Model for Histopathology Images
This addresses the problem of reducing annotation burden for pathologists in computational pathology, though it is incremental as it builds on existing methods with a novel conditioning approach.
The paper tackles nuclei detection and classification in histopathology images by introducing TAND, a framework that uses tissue mask conditioning with point-level supervision, achieving state-of-the-art performance on the PUMA benchmark with notable improvements in tissue-dependent cell types.
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.