Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images
This research addresses the problem of cross-domain adaptation for few-shot learning, which is important for applications like medical imaging, but it is incremental as it evaluates existing models without introducing new methods.
The study investigated the performance of few-shot classification models across natural and histopathological images, revealing insights into their transferability and generalization capabilities between these domains.
In this study, we investigate the performance of few-shot classification models across different domains, specifically natural images and histopathological images. We first train several few-shot classification models on natural images and evaluate their performance on histopathological images. Subsequently, we train the same models on histopathological images and compare their performance. We incorporated four histopathology datasets and one natural images dataset and assessed performance across 5-way 1-shot, 5-way 5-shot, and 5-way 10-shot scenarios using a selection of state-of-the-art classification techniques. Our experimental results reveal insights into the transferability and generalization capabilities of few-shot classification models between diverse image domains. We analyze the strengths and limitations of these models in adapting to new domains and provide recommendations for optimizing their performance in cross-domain scenarios. This research contributes to advancing our understanding of few-shot learning in the context of image classification across diverse domains.