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Few-Shot Cross-Dataset Adaptation for Tuberculosis Detection Using DenseNet
Author1, Author2, Author3, Author4, Author5
fine-tuningdomain adaptationfew-shot learning
2608.21427
Builder Relevance
3h ago80%
Abstract
This paper addresses the challenge of domain adaptation in tuberculosis detection using few-shot learning techniques.
Reality Card
Core Claim
The study demonstrates that full fine-tuning of a pretrained DenseNet121 model can achieve 98.36% accuracy with just 75 labeled samples per class, effectively mitigating domain shift.
Method / Result
Achieved 98.36% accuracy with only 75 labeled samples per class.
Limitations
The study may face challenges in reproducibility due to variations in imaging protocols and patient demographics across datasets.
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