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
Early Fusion of Features for Semantic Segmentation
Anupam Gupta, Ashok Krishnamurthy, Lisa Singh
ClickSAM: Fine-tuning Segment Anything Model using click prompts for ultrasound image segmentation
Aimee Guo, Grace Fei, Hemanth Pasupuleti, Jing Wang
On the Effect of Image Resolution on Semantic Segmentation
Ritambhara Singh, Abhishek Jain, Pietro Perona, Shivani Agarwal, Junfeng Yang