S2S: A deep learning method for the radiological diagnosis of fine-grained diseases spanning screening to subtyping.

ABSTRACT: Radiographic images play a critical role in disease diagnosis, but accurately interpreting them requires considerable expertise and workload. Recent research has advanced artificial intelligence-based medical image analysis, but such advancements remain limited in real clinical practice where multistage diagnosis is required for fine-grained diseases. This study proposes a screening-to-subtyping (S2S) AI paradigm specifically designed for accurate radiological diagnosis of fine-grained diseases, encompassing the entire diagnostic process from initial screening to final subtyping. The S2S framework integrates information from multiple diagnostic phases, radiological viewpoints, lesion dimensions, and imaging modalities to address complex diagnostic challenges. Evaluation using a large-scale

SUBMITTER: Tang R 

PROVIDER: S-EPMC12546454 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature