Segmentation Accuracy
Segmentation accuracy, the precision of delineating objects or regions within images, is a crucial aspect of many fields, particularly medical image analysis and remote sensing. Current research focuses on improving accuracy through advanced model architectures like U-Net and its variants, Transformers, and novel loss functions designed to address challenges such as class imbalance and small object detection. These advancements are driving improvements in diagnostic accuracy, treatment planning, and automated analysis across diverse applications, impacting both scientific understanding and practical outcomes.
Papers
Enhancing Ecological Monitoring with Multi-Objective Optimization: A Novel Dataset and Methodology for Segmentation Algorithms
Sophia J. Abraham, Jin Huang, Brandon RichardWebster, Michael Milford, Jonathan D. Hauenstein, Walter Scheirer
CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation
Xiao Liu, Peng Gao, Tao Yu, Fei Wang, Ru-Yue Yuan