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Skip Connections

Direct links that bypass one or more network layers, allowing the preservation of fine spatial information in the encoder and transferring it to the decoder.

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Mirror Padding

Border padding technique using pixel reflection, used in U-Net to prevent information loss at edges during convolutions without padding.

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Dice Loss

Cost function based on the Dice coefficient, optimized for imbalanced segmentation problems by measuring the overlap between prediction and ground truth.

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Multi-class Segmentation

Extension of U-Net to simultaneously predict multiple object classes, using an output layer with softmax activation and one-hot encoding of labels.

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U-Net++ (Nested U-Net)

Improved variant of U-Net introducing nested skip connections and dimension reduction blocks, reducing vanishing gradient and improving feature fusion.

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Attention U-Net

U-Net architecture augmented with attention mechanisms that weight skip connection features, allowing the model to focus on the most relevant structures.

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Intersection over Union (IoU) Score

Evaluation metric measuring the ratio between the intersection and union of predicted and actual segmentation masks, standard for evaluating U-Net performance.

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Convolutional Block

Basic unit of U-Net composed of two successive 3x3 convolutions with ReLU activation and batch normalization, followed by a pooling operation in the encoder.

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