Training Segmentation

Training segmentation models for image analysis focuses on accurately delineating objects or regions within images, often requiring substantial labeled data. Current research emphasizes overcoming data limitations through techniques like generative adversarial networks to synthesize training data, self-supervised learning methods that leverage unlabeled data, and weakly-supervised approaches using text or other auxiliary information. These advancements are crucial for improving the efficiency and applicability of segmentation in diverse fields, particularly medical imaging, where labeled data is scarce and expensive to obtain, enabling more accurate and cost-effective diagnoses and treatment planning.