Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search.

Area of Science:

• Medical image analysis
• Computer vision
• Machine learning

Background:

• Automating White Blood Cell (WBC) classification from microscopic images is crucial for efficient diagnosis.
• Deep learning models face performance degradation due to domain shift caused by varying imaging conditions.
• Unsupervised Domain Adaptation (UDA) methods typically require substantial unlabeled target data, which is often unavailable in medical imaging.

Purpose of the Study:

• To develop a UDA method for WBC classification that does not require any target data.
• To address the challenge of domain shift in microscopic image analysis without relying on unlabeled target samples.

Main Methods:

• Proposes a novel UDA technique that identifies a 'closest-clone' from source data for a given target test image.
• Utilizes a latent-variable generative model based on variational inference for simultaneous sampling and closest-clone identification.
• Employs an optimization procedure in the latent space to find the closest source data proxy.

Main Results:

• Demonstrates the efficacy of the proposed method over State-Of-The-Art (SOTA) UDA techniques.
• Achieves robust WBC classification performance across datasets captured with different imaging modalities and settings.
• Validates the method's ability to overcome domain shift without target data.

Conclusions:

• The proposed data-free UDA method effectively handles domain shift in WBC classification.
• This approach offers a promising solution for medical image analysis where labeled or unlabeled target data is scarce.
• The method advances automated diagnostic tools by improving model generalizability across diverse imaging environments.