Computer Vision
Semantic Segmentation — Pixel-Level Understanding of Images
Semantic segmentation assigns a class label to every pixel in an image, enabling fine-grained scene understanding. It is critical for autonomous driving, medical imaging, and robotics.
- U-Net Dominates Medical — Encoder-decoder with skip connections excels on small medical imaging datasets
- Dice + BCE is Standard — Combined loss functions handle class imbalance and optimize overlap directly
- mIoU is the Metric — Mean Intersection over Union across all classes is the primary evaluation measure
Semantic Segmentation — FCN, U-Net, DeepLab and Medical Imaging
Semantic segmentation assigns a class label to every pixel in an image, enabling fine-grained scene understanding. It is critical for autonomous driving, medical imaging, and robotics.
Segmentation Types
Fully Convolutional Network (FCN)
U-Net Architecture
DeepLab
Loss Functions for Segmentation
Evaluation: IoU
Applications
Summary
- Semantic segmentation assigns a class label to every pixel
- FCN introduced fully convolutional architecture for pixel-wise prediction
- U-Net with skip connections dominates medical imaging
- DeepLab uses dilated convolution for multi-scale context
- Dice + BCE loss handles class imbalance
- mIoU is the standard evaluation metric
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