{"slug": "automated-nucleus-segmentation-in-histology-images-an-attention-u-net-approach", "title": "Automated Nucleus Segmentation in Histology Images: An Attention U-Net Approach with Comprehensive Preprocessing and Inference Pipelines", "summary": "A lightweight Attention U-Net model trained on the MoNuSeg 2018 dataset achieved an Intersection over Union (IoU) score of 0.8745 on full-resolution, unseen histology test images, according to arXiv paper 2610.31902v1. The pipeline combines stain deconvolution and multi-pass mask smoothing with an overlapping patch-stitching strategy for inference on high-resolution images. The authors conclude Attention U-Net is an effective and competitive method for automated, scalable nucleus delineation in clinical pathology.", "body_md": "arXiv:2610.31902v1 Announce Type: new \nAbstract: Objectives: In the area of digital pathology, nucleus segmentation is an essential step for quantitative analysis; however, high nuclear density and staining heterogeneity in histology images pose a great challenge.\n  Methods: This study implements a lightweight Attention U-Net model on the MoNuSeg 2018 dataset to overcome these challenges. The proposed pipeline integrates comprehensive image and mask preprocessing, including stain deconvolution and multi-pass mask smoothing, with a high-fidelity overlapping patch-stitching strategy during inference on high-resolution images.\n  Results: The model achieved an Intersection over Union (IoU) score of 0.8745 on full-resolution, unseen test images. This result demonstrates the effectiveness of attention mechanisms in enhancing feature localization and validates the stitching methodology for generating seamless and accurate segmentation maps.\n  Conclusion: Attention U-Net is an effective and competitive method for automated, scalable nucleus delineation in challenging clinical contexts.", "url": "https://wpnews.pro/news/automated-nucleus-segmentation-in-histology-images-an-attention-u-net-approach", "canonical_source": "https://arxiv.org/abs/2610.31902", "published_at": "2026-10-26 04:00:00+00:00", "updated_at": "2026-10-26 04:12:15.409371+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "ai-research", "artificial-intelligence"], "entities": ["Attention U-Net", "MoNuSeg 2018", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/automated-nucleus-segmentation-in-histology-images-an-attention-u-net-approach", "markdown": "https://wpnews.pro/news/automated-nucleus-segmentation-in-histology-images-an-attention-u-net-approach.md", "text": "https://wpnews.pro/news/automated-nucleus-segmentation-in-histology-images-an-attention-u-net-approach.txt", "jsonld": "https://wpnews.pro/news/automated-nucleus-segmentation-in-histology-images-an-attention-u-net-approach.jsonld"}}