# WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological Attributes

arXiv: https://arxiv.org/abs/2306.13531 · HTML: https://mlsanity.com/paper/2306.13531/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 74 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [GANSpace: Discovering Interpretable GAN Controls](https://mlsanity.com/paper/2004.02546/) (2020)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation](https://mlsanity.com/paper/2011.12799/) (2020)
- [Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging](https://mlsanity.com/paper/1909.12475/) (2019)
- [Are Transformers More Robust Than CNNs?](https://mlsanity.com/paper/2111.05464/) (2021)
- [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019)
- [Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates](https://mlsanity.com/paper/1910.03231/) (2019)
- [Disparities in Dermatology AI: Assessments Using Diverse Clinical Images](https://mlsanity.com/paper/2111.08006/) (2021)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [Explaining in Style: Training a GAN to explain a classifier in StyleSpace](https://mlsanity.com/paper/2104.13369/) (2021)
- [Chest ImaGenome Dataset for Clinical Reasoning](https://mlsanity.com/paper/2108.00316/) (2021)
- [Explainable Object-induced Action Decision for Autonomous Vehicles](https://mlsanity.com/paper/2003.09405/) (2020)
- [SkinCon: A skin disease dataset densely annotated by domain experts for fine-grained model debugging and analysis](https://mlsanity.com/paper/2302.00785/) (2023)
- [Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition](https://mlsanity.com/paper/2210.06871/) (2022)
- [MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction](https://mlsanity.com/paper/2301.00345/) (2023)
- [Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products](https://mlsanity.com/paper/1803.07679/) (2018)

## Built on by (most cited first, 22 of 22)

- [Semi-supervised Concept Bottleneck Models](https://mlsanity.com/paper/2406.18992/) (2024)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [Integrating Clinical Knowledge into Concept Bottleneck Models](https://mlsanity.com/paper/2407.06600/) (2024)
- [Improving Concept Alignment in Vision-Language Concept Bottleneck Models](https://mlsanity.com/paper/2405.01825/) (2024)
- [A Large-scale Multi Domain Leukemia Dataset for the White Blood Cells Detection with Morphological Attributes for Explainability](https://mlsanity.com/paper/2405.10803/) (2024)
- [Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification](https://mlsanity.com/paper/2505.14049/) (2025)
- [DAFFNet: A Dual Attention Feature Fusion Network for Classification of White Blood Cells](https://mlsanity.com/paper/2405.16220/) (2024)
- [Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data](https://mlsanity.com/paper/2504.09899/) (2025)
- [Uni-Hema: Unified Model for Digital Hematopathology](https://mlsanity.com/paper/2511.13889/) (2025)
- [Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset](https://mlsanity.com/paper/2504.02602/) (2025)
- [Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2507.07148/) (2025)
- [AttriGen: Automated Multi-Attribute Annotation for Blood Cell Datasets](https://mlsanity.com/paper/2509.26185/) (2025)
- [Weakly Supervised Concept Learning with Class-Level Priors for Interpretable Medical Diagnosis](https://mlsanity.com/paper/2511.01131/) (2025)
- [Uncertainty-Aware Information Pursuit for Interpretable and Reliable Medical Image Analysis](https://mlsanity.com/paper/2506.16742/) (2025)
- [Chat-CBM: Towards Interactive Concept Bottleneck Models with Frozen Large Language Models](https://mlsanity.com/paper/2509.17522/) (2025)
- [BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models](https://mlsanity.com/paper/2510.15866/) (2025)
- [Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following](https://mlsanity.com/paper/2603.19482/) (2026)
- [DCG-Net: Dual Cross-Attention with Concept-Value Graph Reasoning for Interpretable Medical Diagnosis](https://mlsanity.com/paper/2603.20325/) (2026)
- [SciLT: Long-tailed Image Classification under Scientific Image Domains](https://mlsanity.com/paper/2604.03687/) (2026)
- [WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images](https://mlsanity.com/paper/2605.19692/) (2026)
- [Multilingual Hematology Visual Question Answering Dataset](https://mlsanity.com/paper/2606.25246/) (2026)
- [Grad-CAM for Vision Transformers: A Systematic Taxonomy and Audit of Methodological Ambiguity in Explainable AI](https://mlsanity.com/paper/2608.05258/) (2026)

## Abstract (arXiv)

The examination of blood samples at a microscopic level plays a fundamental role in clinical diagnostics, influencing a wide range of medical conditions. For instance, an in-depth study of White Blood Cells (WBCs), a crucial component of our blood, is essential for diagnosing blood-related diseases such as leukemia and anemia. While multiple datasets containing WBC images have been proposed, they mostly focus on cell categorization, often lacking the necessary morphological details to explain such categorizations, despite the importance of explainable artificial intelligence (XAI) in medical domains. This paper seeks to address this limitation by introducing comprehensive annotations for WBC images. Through collaboration with pathologists, a thorough literature review, and manual inspection of microscopic images, we have identified 11 morphological attributes associated with the cell and its components (nucleus, cytoplasm, and granules). We then annotated ten thousand WBC images with these attributes. Moreover, we conduct experiments to predict these attributes from images, providing insights beyond basic WBC classification. As the first public dataset to offer such extensive annotations, we also illustrate specific applications that can benefit from our attribute annotations. Overall, our dataset paves the way for interpreting WBC recognition models, further advancing XAI in the fields of pathology and hematology.