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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Leveraging sparse annotations for leukemia diagnosis on the large leukemia dataset.

Abdul Rehman1, Talha Meraj1, Aiman Mahmood Minhas2

  • 1Intelligent Machine Lab, Information Technology University of Punjab, Lahore, Pakistan.

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Summary

This study introduces the Large Leukemia Dataset (LLD) for improved white blood cell (WBC) analysis and presents novel deep learning methods for accurate leukemia cell detection and attribute prediction, enhancing diagnostic capabilities.

Keywords:
AttributeBlood cancerExplainable diagnosisLeukemiaMulti-task learningSparse annotation

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Hematology

Background:

  • Leukemia is a major global health concern, necessitating accurate white blood cell (WBC) analysis for diagnosis.
  • Current deep learning models for leukemia analysis are limited by small, non-diverse datasets, hindering real-world application.
  • Existing datasets lack the scale and domain diversity required for robust WBC localization, classification, and morphological assessment.

Purpose of the Study:

  • To address the limitations of existing datasets by introducing a large-scale, diverse dataset for leukemia analysis.
  • To develop novel deep learning methods for accurate WBC detection and morphological attribute prediction.
  • To reduce the annotation burden on medical experts through sparse annotation techniques.

Main Methods:

  • A large-scale dataset, the 'Large Leukemia Dataset' (LLD), was created from Peripheral Blood Films (PBF) of 48 patients, using diverse microscopy and imaging equipment.
  • Each leukemia cell was annotated with 7 morphological attributes at 100x magnification to enhance diagnostic explainability.
  • A multi-task deep learning model was proposed for simultaneous WBC detection and attribute prediction, alongside a sparse annotation method for efficient learning.

Main Results:

  • The developed Large Leukemia Dataset (LLD) provides a comprehensive resource for leukemia research and development.
  • The multi-task model demonstrated effective WBC detection and attribute prediction, offering interpretable and clinically relevant insights.
  • The sparse annotation method improved learning efficiency and diagnostic accuracy by utilizing the entire field of view.

Conclusions:

  • The Large Leukemia Dataset (LLD) and proposed methods offer significant advancements in automated leukemia analysis.
  • The developed techniques enhance diagnostic explainability and address domain-shift challenges in microscopic image analysis.
  • The publicly available dataset, code, and demo facilitate further research and development in computational hematology.