Beyond Labels: Visual Representations for Bone Marrow Cell Morphology Recognition

TL;DR

This paper introduces a self-supervised learning approach for bone marrow cell recognition, overcoming data annotation challenges and improving accuracy over existing methods in medical image analysis.

Contribution

It presents a novel self-supervised training methodology that enhances bone marrow cell recognition without relying solely on labeled data.

Findings

Significant performance improvements over state-of-the-art methods.

Effective handling of class imbalance in medical datasets.

Demonstrated robustness in identifying diverse cell types.

Abstract

Analyzing and inspecting bone marrow cell cytomorphology is a critical but highly complex and time-consuming component of hematopathology diagnosis. Recent advancements in artificial intelligence have paved the way for the application of deep learning algorithms to complex medical tasks. Nevertheless, there are many challenges in applying effective learning algorithms to medical image analysis, such as the lack of sufficient and reliably annotated training datasets and the highly class-imbalanced nature of most medical data. Here, we improve on the state-of-the-art methodologies of bone marrow cell recognition by deviating from sole reliance on labeled data and leveraging self-supervision in training our learning models. We investigate our approach's effectiveness in identifying bone marrow cell types. Our experiments demonstrate significant performance improvements in conducting different bone marrow cell recognition tasks compared to the current state-of-the-art methodologies.