WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images

• URL: http://arxiv.org/abs/2605.19692v1
• Date: Tue, 19 May 2026 11:25:25 GMT
• Title: WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images
• Abstract summary: WBCAtt+ is a novel dataset of WBC images densely annotated with 11 morphological attributes and five pixel-level cell components.<n>With 113k image-level labels and 10k segmentation maps, WBCAtt+ is the first to provide comprehensive annotations for WBC images.
• Score: 59.418050353415005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The microscopic examination of white blood cells (WBCs) plays a fundamental role in pathology and is essential for diagnosing blood disorders such as leukemia and anemia. To support further research on WBC images, multiple datasets have been proposed. However, they mainly annotate cell categories, and lack detailed morphological characteristics that pathologists use to explain their interpretations of cells. To address this gap, we introduce WBCAtt+, a novel dataset of WBC images densely annotated with 11 morphological attributes and five pixel-level cell components. With 113k image-level labels and 10k segmentation maps, WBCAtt+ is the first to provide comprehensive annotations for WBC images. Leveraging this dataset, we provide baseline models for attribute recognition and semantic segmentation. We also design an attribute recognition model to incorporate compositional structure of cells, further improving the recognition performance. Lastly, we showcase various applications enabled by our dataset, such as explainable AI models, including counterfactual example generation. \revision{The dataset and code are publicly available\footnote{https://doi.org/10.57967/hf/8143}}.

Related papers

• CellPainTR: Generalizable Representation Learning for Cross-Dataset Cell Painting Analysis [51.56484100374058]
  We introduce CellPainTR, a Transformer-based architecture designed to learn foundational representations of cellular morphology.<n>Our work represents a significant step towards creating truly foundational models for image-based profiling, enabling more reliable and scalable cross-study biological analysis.
  arXiv  Detail & Related papers  (2025-09-02T03:30:07Z)
• Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset [44.948939549346676]
  Leukemia is the 10th most frequently diagnosed cancer and one of the leading causes of cancer-related deaths worldwide.<n>Despite deep learning advances in medical imaging, leukemia analysis lacks a large, diverse multi-task dataset.<n>We present a large-scale WBC dataset and novel methods for detecting WBC with their attributes.
  arXiv  Detail & Related papers  (2025-04-03T14:04:02Z)
• DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images [105.46086313858062]
  We introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks.<n>DiffKillR efficiently propagates annotations across large microscopy images, reducing the need for extensive manual labeling.<n>We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods.
  arXiv  Detail & Related papers  (2024-10-04T00:38:29Z)
• DAFFNet: A Dual Attention Feature Fusion Network for Classification of White Blood Cells [2.0005570775461567]
  We propose a novel dual-branch network Dual Attention Feature Fusion Network (DAFFNet), which integrates the high-level semantic features with morphological features of WBC. Our proposed network framework achieves 98.77%, 91.30%, 98.36%, 99.71%, 98.45%, and 98.85% overall accuracy on the six public datasets.
  arXiv  Detail & Related papers  (2024-05-25T13:09:25Z)
• 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)
• WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes [22.423647778787334]
  This paper introduces comprehensive annotations for White Blood Cells (WBCs) images. We have identified 11 morphological attributes associated with the cell and its components. We conduct experiments to predict these attributes from images, providing insights beyond basic WBC classification.
  arXiv  Detail & Related papers  (2023-06-23T14:52:37Z)
• Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images [52.527733226555206]
  We investigate the use of four attribution methods to explain a multiple instance learning models. We study two datasets of acute myeloid leukemia with over 100 000 single cell images. We compare attribution maps with the annotations of a medical expert to see how the model's decision-making differs from the human standard.
  arXiv  Detail & Related papers  (2023-03-15T14:00:11Z)
• Semantic Segmentation of Anaemic RBCs Using Multilevel Deep Convolutional Encoder-Decoder Network [2.5398817423053037]
  We propose a convolutional neural network (CNN) model for semantic segmentation of red blood cells. The proposed model preserved pixel-level semantic information extracted in one layer and then passed to the next layer to choose relevant features. This phenomenon helps to precise pixel-level counting of healthy and anaemic-RBC elements along with morphological analysis.
  arXiv  Detail & Related papers  (2022-02-09T17:31:50Z)
• Classification of White Blood Cell Leukemia with Low Number of Interpretable and Explainable Features [0.0]
  White Blood Cell (WBC) Leukaemia is detected through image-based classification. Convolutional Neural Networks are used to learn the features needed to classify images of cells a malignant or healthy. This type of model requires learning a large number of parameters and is difficult to interpret and explain. We present an XAI model which uses only 24 explainable and interpretable features and is highly competitive to other approaches by outperforming them by about 4.38%.
  arXiv  Detail & Related papers  (2022-01-28T00:08:56Z)

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.