Meningioma Analysis and Diagnosis using Limited Labeled Samples

TL;DR

This paper introduces an adaptive feature fusion method for classifying meningiomas from MRI images using limited labeled samples, demonstrating superior performance over existing methods.

Contribution

The study proposes a novel adaptive feature fusion architecture that combines spatial and frequency domain features for few-shot meningioma classification.

Findings

Outperforms state-of-the-art methods on three datasets

Introduces a new MRI dataset for meningioma analysis

Demonstrates the importance of frequency band contribution in classification

Abstract

The biological behavior and treatment response of meningiomas depend on their grade, making an accurate diagnosis essential for treatment planning and prognosis assessment. We observed that the weighted fusion of spatial-frequency domain features significantly influences meningioma classification performance. Notably, the contribution of specific frequency bands obtained by discrete wavelet transform varies considerably across different images. A feature fusion architecture with adaptive weights of different frequency band information and spatial domain information is proposed for few-shot meningioma learning. To verify the effectiveness of the proposed method, a new MRI dataset of meningiomas is introduced. The experimental results demonstrate the superiority of the proposed method compared with existing state-of-the-art methods in three datasets. The code will be available at: https://github.com/ICL-SUST/AMSF-Net