AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.

ABSTRACT:

Background

Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time and tedious manipulations (e.g., 7-15 days) and large-field view (e.g., > 1000 × 1000 μm²). Artificial intelligence has recently revolutionized cancer diagnosis by training pathological image databases via deep learning. Current approaches, however, remain dependent on analyzing wide-field pathological images to detect distinct nuclear, cytologic, and histomorphologic traits for diagnostic categorization, limiting their applicability to minimally invasive lesion.

Results

Herein, we develop a molecular imaging strategy

SUBMITTER: Cao H 

PROVIDER: S-EPMC12001690 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature