Multifeature Ultrasound-Based Classification for Breast Lesions: A Comparative Study of PONS Image Enhancement Technology.

ABSTRACT:

Objective

To overcome critical limitations of B-mode ultrasound in artificial intelligence diagnostics-including poor image quality and operator variability-by developing a multifeature framework that combines raw B-mode scans with 2 optimized representations (enhanced ultrasound and quality-improved ultrasound) for robust breast cancer classification.

Patients and methods

We conducted a retrospective study of 62,912 breast ultrasound scans (100%) from 688 patients (100%) at the Mayo Clinic (from December 01, 1989 to March 30, 2024). The study compared 3 deep learning architectures-graph convolutional networks (GCNs), masked autoencoders (MAEs), and multi-scale convolutional neural network (MSCNN)-using either standard B-mode inputs alone or combined with our enhanced featur

SUBMITTER: Daloglu SM 

PROVIDER: S-EPMC12830096 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature