RadioGuide-DCN: A Radiomics-Guided Decorrelated Network for Medical Image Classification

Lifeng Guo, Ying Fu, Shi Tan, Qi Wang, Yangan Zhang, Xiaohong Huang, Xueguang Yuan · 2025

Read RadioGuide-DCN: A Radiomics-Guided Decorrelated Network for Medical Image Classification by Lifeng Guo, Ying Fu, Shi Tan, Qi Wang, Yangan Zhang, Xiaohong Huang, Xueguang Yuan (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12838146.txt.

Description

Lifeng Guo and six co-authors build a network that classifies medical images by mixing two styles of analysis. Their paper is from two thousand twenty-five. Deep networks learn rich features but can overfit small data sets. Older hand-crafted features, drawn from radiology practice, are stable but can miss subtle signs of disease.

Their model feeds the hand-crafted features in as a guide for the network. A special loss pushes the learned features apart, so they carry less repeated information. A fusion step then blends local detail with the global picture while muting distractions. On one breast image data set, the model reached about ninety-four percent accuracy. It beat both plain radiology features and standard deep networks on several tasks.

Catalog record

CREATORLifeng Guo, Ying Fu, Shi Tan, Qi Wang, Yangan Zhang, Xiaohong Huang, Xueguang Yuan
YEAR2025
COLLECTIONPAPERS
FORMATTXT
FILE SIZE56 KB
VERSION1.0
LICENSECC BY 4.0
RIGHTSCC BY
SOURCEEurope PMC

Source: https://europepmc.org/articles/PMC12838146

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