Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation.

ABSTRACT:

Purpose

Fluorescence in situ hybridization (FISH) plays a critical role in cancer screening but faces challenges in signal clarity and manual intervention. This study aims to enhance FISH signal clarity, improve screening efficiency, and reduce false negatives through an automated image acquisition and signal enhancement framework.

Methods

An automated workflow was developed, integrating a dynamic signal enhancement method that optimizes global and local features. An improved Cycle-GAN network was introduced, incorporating residual connections and layer-wise supervision to accurately model and compensate for complex signal characteristics. Key metrics such as signal brightness, edge gradients, contrast improvement index (CII), and structural similarity index (SSIM) were used

SUBMITTER: Shi L 

PROVIDER: S-EPMC11897095 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature