Unsupervised Domain Adaptation with SAM-RefiSeR for Enhanced Brain Tumor Segmentation
Dillan Imans, Phuoc-Nguyen Bui, Duc-Tai Le, Hyunseung Choo
TL;DR
This paper introduces SAM-RefiSeR, a novel unsupervised domain adaptation method that improves brain tumor segmentation accuracy across different datasets without requiring labeled target data.
Contribution
It presents a new unsupervised domain adaptation framework specifically designed for brain tumor segmentation, enhancing model generalization across diverse datasets.
Findings
Significant improvement in segmentation accuracy on unseen datasets.
Effective reduction of domain shift effects.
Outperforms existing unsupervised adaptation methods.
Abstract
Unsupervised Domain Adaptation with SAM-RefiSeR for Enhanced Brain Tumor Segmentation
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Taxonomy
TopicsAdvanced Neural Network Applications · Brain Tumor Detection and Classification · Domain Adaptation and Few-Shot Learning