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Multimodal MRI-Based Unsupervised Brain Tumor Detection Using Modality Translation and Anomaly Discrimination: A

Fanrui Meng¹, Tao Yang¹, Lisheng Wang¹

¹School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China (F.M., T.Y., L.W.).

Academic Radiology

|April 23, 2026

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This study introduces an unsupervised brain tumor detection method using only healthy MRI scans, reducing the need for extensive manual annotation. The novel approach achieves high accuracy in identifying tumors across various types and centers.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate brain tumor detection via MRI is crucial for diagnosis and treatment.
Supervised models require costly and time-consuming annotation of large tumor datasets.
Unsupervised methods offer a potential solution to reduce annotation burden.

Purpose of the Study:

To develop an unsupervised brain tumor detection method using only healthy MRI samples.
To leverage multimodal MRI characteristics for improved detection accuracy.
To reduce the reliance on extensive manual annotation for training AI models.

Main Methods:

A modality translating network (MTN) and anomaly discriminating network (ADN) were trained on healthy T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI).

Keywords:

Brain MRI Modality Translation Unsupervised Tumor Detection

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The MTN translates healthy T1WI to T2WI; the ADN detects anomalies by comparing synthesized and real T2WI.

During validation, the model identifies tumors by detecting discrepancies between translated lesion-free T2WI and original tumor-containing T2WI.

Main Results:

The proposed unsupervised model achieved an average precision (AP) of 77% and a Dice similarity coefficient (DSC) of 54%.
Performance significantly outperformed state-of-the-art unsupervised methods by 0.17 in AP and 0.06 in DSC.
The model demonstrated robust detection across multiple centers and tumor types.

Conclusions:

The developed model effectively detects brain tumors using only healthy MRI data, significantly reducing annotation efforts.
The approach shows strong generalizability across different centers and tumor types.
Utilizing multimodal MRI data enhances brain tumor detection accuracy and model generalization.