A Few-shot Multimodal Deep Learning Framework For Precision Diagnosis Of Parotid Gland

Summary

This article presents a few-shot multimodal deep learning framework for accurate classification of parotid gland tumors using magnetic resonance imaging sequences.

Abstract

Accurate differentiation between benign and malignant parotid tumors is imperative for patient prognosis. However, due to the high degree of heterogeneity of parotid tumors and the limited availability of imaging data, preoperative diagnosis remains a significant challenge. To address this issue, the present study proposes a multimodal deep learning framework based on small-sample learning. This framework integrates multi-sequence MRI information and incorporates a multi-scale spatial attention mechanism to improve feature extraction and classification performance in situations where samples are limited. The model was systematically evaluated using a retrospectively collected cohort of 198 patients with histopathologically confirmed primary parotid gland tumors (107 benign, 91 malignant) who underwent complete preoperative MRI—including T1-weighted, T2-weighted, and diffusion-weighted imaging—at our institution, with no prior treatment for the parotid lesion. The cohort was split at the patient level into training (70%), validation (15%), and test (15%) sets using stratified random sampling to preserve the benign-to-malignant ratio, ensuring mutually exclusive subsets and preventing data leakage. The model achieved an area under the curve (AUC) of 0.9822. This finding underscores the system’s capacity to differentiate between benign and malignant tumors. Moreover, the model exhibited substantial advantages in terms of diagnostic accuracy when compared to experienced radiologists, underscoring its potential application in preoperative benign-malignant differentiation and clinical decision support for parotid tumors.

Introduction

Epidemiological data indicate that the incidence of parotid gland tumors is increasing. The parotid gland, the largest salivary gland in the human body¹, is one of the high-incidence sites for head and neck tumors. While parotid gland tumors are relatively uncommon, accounting for approximately 5% of head and neck tumors, benign lesions far outnumber malignant ones, constituting about 80% of all parotid gland tumors^2,3. Benign and malignant parotid tumors exhibit significant differences in biological behavior, treatment strategies, and prognosis. Benign tumors typically grow slowly, exh....

Protocol

All patient data used in this study were collected exclusively at Wuxi People’s Hospital and consisted of 198 pathologically confirmed parotid tumor cases. This study was approved by the Institutional Review Board of Wuxi People’s Hospital (IRB No. KY24068) and conducted in full accordance with institutional guidelines and international ethical standards. All data were fully anonymized prior to analysis.

MRI Image acquisition

All MRI sequences (T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging) were acquired on a 3.0 Tesla MRI scanner (Siemens MAGNETOM Verio or Prisma) usin....

Results

Model Performance Overview

In order to evaluate the diagnostic performance of the model, a set of standard metrics was employed, including accuracy, precision, recall, and F1-score. Under the 2-way 5-shot configuration, the multimodal few-shot learning model based on R3D-18 achieved the following results on the test set for the classification of parotid gland tumors: accuracy of 96.88%, precision of 97.50%, recall of 96.88%, and F1-score of 96.83%. The confusion matrix shown i.......

Discussion

In this study, we hypothesized that a multimodal few-shot deep learning framework, which integrates multi-sequence MRI (T1-weighted, T2-weighted, and diffusion-weighted imaging) with a multi-scale spatial attention mechanism, can accurately differentiate benign and malignant parotid gland tumors under limited annotated data conditions and outperform both conventional deep learning approaches and experienced radiologists.

The experimental results strongly support this hypothesis. On the indepen.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Grad-CAM			https://github.com/jacobgil/pytorch-grad-cam
NumPy 1.25	NumPy Developers		https://numpy.org/
pandas 2.1	Pandas Developers		https://pandas.pydata.org/
Pycharm	JetBrains		https://www.jetbrains.com/zh-cn/pycharm/
Python 3.10	Python Software Foundation		https://www.python.org/
PyTorch 2.1	Meta Platforms, Inc.		https://pytorch.org/
scikit-learn 1.3	Scikit-learn Developers		https://scikit-learn.org/
SHAP			https://github.com/slundberg/shap
Siemens MAGNETOM Prisma 3.0T Magnetic Resonance Imaging System	Siemens Healthineers		https://www.siemens-healthineers.cn/magnetic-resonance-imaging/3t-mri-scanner/magnetom-prisma