Anatomical Partition-Based Deep Learning: An Automatic Nasopharyngeal MRI Recognition Scheme.

ABSTRACT:

Background

Training deep learning (DL) models to automatically recognize diseases in nasopharyngeal MRI is a challenging task, and optimizing the performance of DL models is difficult.

Purpose

To develop a method of training anatomical partition-based DL model which integrates knowledge of clinical anatomical regions in otorhinolaryngology to automatically recognize diseases in nasopharyngeal MRI.

Study type

Single-center retrospective study.

Population

A total of 2485 patients with nasopharyngeal diseases (age range 14-82 years, female, 779[31.3%]) and 600 people with normal nasopharynx (age range 18-78 years, female, 281[46.8%]) were included.

Sequence

3.0 T; T2WI fast spin-echo sequence.

Assessment

Full images (512 × 512) of 3085 patients const

SUBMITTER: Li S 

PROVIDER: S-EPMC9541866 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature