Adaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images

• URL: http://arxiv.org/abs/2511.13586v2
• Date: Tue, 18 Nov 2025 19:43:44 GMT
• Title: Adaptive Multi-Scale Integration Unlocks Robust Cell Annotation in Histopathology Images
• Authors: Yinuo Xu, Yan Cui, Mingyao Li, Zhi Huang,
• Abstract summary: We build a marker-guided dataset from Xenium spatial transcriptomics with single-cell resolution labels for more than two million cells across eight organs.<n>We introduce NuClass, a pathologist workflow inspired framework for cell-wise multi-scale integration of nuclear morphology and microenvironmental context.<n>Our results demonstrate that multi-scale, uncertainty-aware fusion can bridge the gap between slide-level pathological foundation models and reliable, cell-level phenotype prediction.
• Score: 3.504506659662406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying cell types and subtypes in routine histopathology is fundamental for understanding disease. Existing tile-based models capture nuclear detail but miss the broader tissue context that influences cell identity. Current human annotations are coarse-grained and uneven across studies, making fine-grained, subtype-level classification difficult. In this study, we build a marker-guided dataset from Xenium spatial transcriptomics with single-cell resolution labels for more than two million cells across eight organs and 16 classes to address the lack of high-quality annotations. Leveraging this data resource, we introduce NuClass, a pathologist workflow inspired framework for cell-wise multi-scale integration of nuclear morphology and microenvironmental context. It combines Path local, which focuses on nuclear morphology from 224x224 pixel crops, and Path global, which models the surrounding 1024x1024 pixel neighborhood, through a learnable gating module that balances local and global information. An uncertainty-guided objective directs the global path to prioritize regions where the local path is uncertain, and we provide calibrated confidence estimates and Grad-CAM maps for interpretability. Evaluated on three fully held-out cohorts, NuClass achieves up to 96 percent F1 for its best-performing class, outperforming strong baselines. Our results demonstrate that multi-scale, uncertainty-aware fusion can bridge the gap between slide-level pathological foundation models and reliable, cell-level phenotype prediction.

Related papers

• ITC-RWKV: Interactive Tissue-Cell Modeling with Recurrent Key-Value Aggregation for Histopathological Subtyping [2.99938892718088]
  We propose a dual-stream architecture that models the interplay between macroscale tissue features and aggregated cellular representations.<n>We introduce a bidirectional tissue-cell interaction module to enable mutual attention between localized cellular cues and their surrounding tissue environment.
  arXiv  Detail & Related papers  (2025-10-24T14:03:52Z)
• Teacher-Student Model for Detecting and Classifying Mitosis in the MIDOG 2025 Challenge [0.5794811300616634]
  Counting mitotic figures is time-intensive for pathologists and leads to inter-observer variability.<n>Artificial intelligence (AI) promises a solution by automatically detecting mitotic figures while maintaining decision consistency.<n>We formulate mitosis detection as a pixel-level segmentation and propose a teacher-student model that simultaneously addresses mitosis detection and classification.
  arXiv  Detail & Related papers  (2025-09-03T18:08:11Z)
• CellVerse: Do Large Language Models Really Understand Cell Biology? [74.34984441715517]
  We introduce CellVerse, a unified language-centric question-answering benchmark that integrates four types of single-cell multi-omics data.<n>We systematically evaluate the performance across 14 open-source and closed-source LLMs ranging from 160M to 671B on CellVerse.
  arXiv  Detail & Related papers  (2025-05-09T06:47:23Z)
• Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images [7.048241543461529]
  We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification.<n>We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings.<n>A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
  arXiv  Detail & Related papers  (2025-03-13T12:18:37Z)
• CoTCoNet: An Optimized Coupled Transformer-Convolutional Network with an Adaptive Graph Reconstruction for Leukemia Detection [0.3573481101204926]
  We propose an optimized Coupled Transformer Convolutional Network (CoTCoNet) framework for the classification of leukemia. Our framework captures comprehensive global features and scalable spatial patterns, enabling the identification of complex and large-scale hematological features. It achieves remarkable accuracy and F1-Score rates of 0.9894 and 0.9893, respectively.
  arXiv  Detail & Related papers  (2024-10-11T13:31:28Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy [0.20999222360659603]
  We developed a computational framework that can distinguish between normal and metastasizing human cells. The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells.
  arXiv  Detail & Related papers  (2023-09-02T11:20:10Z)
• Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning [20.11220024755348]
  We propose a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators. Our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level.
  arXiv  Detail & Related papers  (2023-05-31T16:54:47Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification [4.642724910208435]
  We propose a multi-stage annotation pipeline to enable the collection of large-scale datasets for histology image analysis. We generate the largest known nuclear instance segmentation and classification dataset, containing nearly half a million labelled nuclei.
  arXiv  Detail & Related papers  (2021-08-25T11:58:52Z)
• Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms [7.229065627904531]
  This study is among the first to hybrid local and global graph methods to profile orchestration and interaction of cellular components. The proposed algorithm achieves a mean diagnosis accuracy of 0.703 with the repeated 5-fold cross-validation scheme.
  arXiv  Detail & Related papers  (2021-06-30T16:09:32Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.