Pathologist-Like Explanations Unveiled: an Explainable Deep Learning
  System for White Blood Cell Classification
Aditya Shankar Pal Debojyoti Biswas Joy Mahapatra Debasis Banerjee Prantar Chakrabarti Utpal Garain
White blood cells (WBCs) play a crucial role in safeguarding the human body
against pathogens and foreign substances. Leveraging the abundance of WBC
imaging data and the power of deep learning algorithms, automated WBC analysis
has the potential for remarkable accuracy. However, the capability of deep
learning models to explain their WBC classification remains largely unexplored.
In this study, we introduce HemaX, an explainable deep neural network-based
model that produces pathologist-like explanations using five attributes:
granularity, cytoplasm color, nucleus shape, size relative to red blood cells,
and nucleus to cytoplasm ratio (N:C), along with cell classification,
localization, and segmentation. HemaX is trained and evaluated on a novel
dataset, LeukoX, comprising 467 blood smear images encompassing ten (10) WBC
types. The proposed model achieves impressive results, with an average
classification accuracy of 81.08% and a Jaccard index of 89.16% for cell
localization. Additionally, HemaX performs well in generating the five
explanations with a normalized mean square error of 0.0317 for N:C ratio and
over 80% accuracy for the other four attributes. Comprehensive experiments
comparing against multiple state-of-the-art models demonstrate that HemaX's
classification accuracy remains unaffected by its ability to provide
explanations. Moreover, empirical analyses and validation by expert
hematologists confirm the faithfulness of explanations predicted by our
proposed model.