While relying heavily on pretrained models for tasks like medical image segmentation offers several advantages such as faster convergence, better generalization to new data, and improved performance on limited datasets; there are potential drawbacks or limitations to consider:
Domain Specificity: Pretrained models may not always capture all relevant features specific to the target domain. Fine-tuning might be necessary but could still lead to suboptimal results.
Overfitting: If the target dataset significantly differs from the pretraining dataset (like ImageNet), there is a risk of overfitting due to biases present in the pretrained weights.
Limited Adaptability: Pretrained models may struggle with adapting well to unique characteristics or nuances present in certain datasets within specialized domains.
Ethical Concerns: Using large-scale datasets like ImageNet raises ethical concerns related to privacy violations or biased representations that could propagate through downstream applications.