Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation

TL;DR

This paper introduces a novel multimodal architecture for leukocyte classification that leverages a new dataset and knowledge distillation to improve performance while maintaining low complexity.

Contribution

The paper presents the first multimodal WBC dataset and develops an efficient architecture enhanced by knowledge distillation for improved classification accuracy.

Findings

Achieved high classification performance with low complexity

Developed a new multimodal WBC dataset

Enhanced learning through knowledge distillation

Abstract

Recently, a lot of automated white blood cells (WBC) or leukocyte classification techniques have been developed. However, all of these methods only utilize a single modality microscopic image i.e. either blood smear or fluorescence based, thus missing the potential of a better learning from multimodal images. In this work, we develop an efficient multimodal architecture based on a first of its kind multimodal WBC dataset for the task of WBC classification. Specifically, our proposed idea is developed in two steps - 1) First, we learn modality specific independent subnetworks inside a single network only; 2) We further enhance the learning capability of the independent subnetworks by distilling knowledge from high complexity independent teacher networks. With this, our proposed framework can achieve a high performance while maintaining low complexity for a multimodal dataset. Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.