CACs Recognition of FISH Images Based on Adaptive Mean Teacher Semi-supervised Learning with Domain-Knowledge Pseudo Label.

Circulating genetically abnormal cells (CACs) serve as crucial biomarkers for lung cancer diagnosis. Detecting CACs holds great value for early diagnosis and screening of lung cancer. To aid the identification of CACs, we have incorporated deep learning algorithms into our CACs detection system, specifically developing algorithms for cell segmentation and signal point detection.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification.

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule.