Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset

• URL: http://arxiv.org/abs/2504.02602v2
• Date: Fri, 08 Aug 2025 23:06:04 GMT
• Title: Leveraging Sparse Annotations for Leukemia Diagnosis on the Large Leukemia Dataset
• Authors: Abdul Rehman, Talha Meraj, Aiman Mahmood Minhas, Ayisha Imran, Mohsen Ali, Waqas Sultani, Mubarak Shah,
• Abstract summary: 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.
• Score: 44.948939549346676
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Leukemia is the 10th most frequently diagnosed cancer and one of the leading causes of cancer-related deaths worldwide. Realistic analysis of leukemia requires white blood cell (WBC) localization, classification, and morphological assessment. Despite deep learning advances in medical imaging, leukemia analysis lacks a large, diverse multi-task dataset, while existing small datasets lack domain diversity, limiting real-world applicability. To overcome dataset challenges, we present a large-scale WBC dataset named Large Leukemia Dataset (LLD) and novel methods for detecting WBC with their attributes. Our contribution here is threefold. First, we present a large-scale Leukemia dataset collected through Peripheral Blood Films (PBF) from 48 patients, through multiple microscopes, multi-cameras, and multi-magnification. To enhance diagnosis explainability and medical expert acceptance, each leukemia cell is annotated at 100x with 7 morphological attributes, ranging from Cell Size to Nuclear Shape. Secondly, we propose a multi-task model that not only detects WBCs but also predicts their attributes, providing an interpretable and clinically meaningful solution. Third, we propose a method for WBC detection with attribute analysis using sparse annotations. This approach reduces the annotation burden on hematologists, requiring them to mark only a small area within the field of view. Our method enables the model to leverage the entire field of view rather than just the annotated regions, enhancing learning efficiency and diagnostic accuracy. From diagnosis explainability to overcoming domain-shift challenges, the presented datasets can be used for many challenging aspects of microscopic image analysis. The datasets, code, and demo are available at: https://im.itu.edu.pk/sparse-leukemiaattri/

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