UNet Transformer

Introduced by Hatamizadeh et al. in UNETR: Transformers for 3D Medical Image Segmentation

UNETR, or UNet Transformer, is a Transformer-based architecture for medical image segmentation that utilizes a pure transformer as the encoder to learn sequence representations of the input volume -- effectively capturing the global multi-scale information. The transformer encoder is directly connected to a decoder via skip connections at different resolutions like a U-Net to compute the final semantic segmentation output.

Source: UNETR: Transformers for 3D Medical Image Segmentation

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Semantic Segmentation	20	18.87%
Image Segmentation	17	16.04%
Medical Image Segmentation	13	12.26%
Tumor Segmentation	6	5.66%
Brain Tumor Segmentation	3	2.83%
Decoder	3	2.83%
Self-Supervised Learning	3	2.83%
Computational Efficiency	2	1.89%
Specificity	2	1.89%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
1x1 Convolution	Convolutions
Batch Normalization	Normalization
Convolution	Convolutions
Multi-Head Attention	Attention Modules
Position-Wise Feed-Forward Layer	Feedforward Networks
ReLU	Activation Functions
Residual Connection	Skip Connections
Scaled Dot-Product Attention	Attention Mechanisms
U-Net	Semantic Segmentation Models

Categories

Add Remove

Medical Image Models