Comparative Analysis of DL methods for Multi-domain Medical Image Segmentation

In this study, we seeked to address the following key questions: • Performance: How do statistical methods, Transformers, zero-shot learning strategies, few-shot finetuning, and low-rank adaptation techniques compare in terms of accuracy and robustness across different medical imaging datasets? • Generalization: To what extent can existing state-of-the-art methods be leveraged to perform inference in unseen settings specifically in the medical domain? • Insights: What meaningful observations can be made from the outcome?