TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification

📰 ArXiv cs.AI

Learn how to apply self-supervised deep learning for brain MRI tumor classification using TumorXAI framework, improving diagnosis and treatment

advanced Published 5 May 2026

Action Steps

Implement TumorXAI framework using ResNet-50 backbone and self-supervised learning (SSL) techniques
Evaluate four SSL frameworks: SimCLR, BYOL, DINO, and Moco v3 on a publicly available dataset
Train the model using self-supervised learning to learn representations from unlabeled data
Fine-tune the pre-trained model on a small labeled dataset for multi-class brain tumor classification
Visualize and interpret the results using explainability techniques to understand tumor classification decisions

Who Needs to Know This

Data scientists and researchers in medical imaging can benefit from this framework to improve brain tumor classification accuracy, while clinicians can use the explainable results for better diagnosis and treatment

Key Insight

💡 Self-supervised learning can be used to improve brain tumor classification accuracy, even with limited annotated datasets

Key Takeaways

Learn how to apply self-supervised deep learning for brain MRI tumor classification using TumorXAI framework, improving diagnosis and treatment

Full Article

Title: TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification

Abstract:
arXiv:2605.01999v1 Announce Type: new Abstract: Classifying brain tumors using magnetic resonance imaging (MRI) is crucial for early diagnosis and treatment; however, tumor heterogeneity and a dearth of annotated datasets restrict the use of supervised deep learning approaches. In this work, we use self-supervised learning (SSL) to study multi-class brain tumor classification. Using a ResNet-50 backbone, we evaluate four SSL frameworks including SimCLR, BYOL, DINO, and Moco v3 on a publicly avai

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