Web Reference: Jan 1, 2026 · To identify gaps and inspire new solutions, this paper offers a comprehensive literature survey of over two hundred deep-learning-based segmentation methods, evaluating their performance across eleven benchmark datasets and common metrics. Apr 1, 2026 · This comprehensive review systematically categorizes and analyzes instance segmentation algorithms across three evolutionary paradigms: CNN-based methods (two-stage and single-stage), transformer-based architectures, and foundation models. As shown in Figure 1, this paper provides a summary of the currently representative deep learning-based medical image segmentation methods, classifying them into three categories based on the learning approach: supervised learning, semi-supervised learning, and unsupervised learning.
YouTube Excerpt: Using a simple example I will explain the difference between image classification, object detection and image
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