Aggregate U-Net for Image Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing encoder-decoder networks for image segmentation face challenges in varying optimal depth requirements based on task difficulty and available labeled data, leading to resource-intensive training and restrictive feature map fusion.
Innovation Solution
The development of an aggregate U-Net architecture comprising multiple U-Nets of varying depths, allowing for training using a series of training iterations and feature map calculations through convolution and up-sampling operations, enabling efficient image segmentation across different categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple encoder-decoder networks of varying depths are trained separately and then aggregated, then the model can adapt to different task difficulties and data availability, but the training process becomes time-consuming and resource intensive
Solution Approach 1:
The patent combines multiple encoder-decoder networks of varying depths into a single aggregate network architecture. Instead of training separate models and aggregating them, the invention merges them into one unified structure that processes inputs through multiple depth levels simultaneously, achieving adaptability while reducing training time and computational resources.
Solution Approach 2:
The aggregate encoder-decoder network is designed to perform multiple functions within a single model by incorporating depth-wise convolutional layers that can operate at different depth levels. This universal architecture can adapt to various task difficulties and data availability conditions without requiring separate trained models for each scenario.
2Device complexity
If skip connections are designed to fuse feature maps from same-scale encoder and decoder pairs, then the network structure remains simple, but the design becomes unnecessarily restrictive and limits feature fusion flexibility
Solution Approach 1:
The patent introduces dynamic skip connections that can adaptively fuse feature maps from different depth levels rather than being restricted to same-scale pairs. The depth-wise convolutional layers dynamically adjust feature fusion based on the specific task requirements, allowing flexible combination of features from various encoder and decoder levels while maintaining manageable network complexity.
Solution Approach 2:
The invention changes the parameters of skip connections by introducing depth-wise convolutional operations that modify how feature maps are fused. Instead of fixed same-scale connections, the skip connections can now operate across different depth levels with adjustable convolution parameters, providing flexibility in feature fusion while controlling structural complexity.
3Device complexity
If a single encoder-decoder network is used, then the model structure is simple and easy to train, but it cannot adapt to varying optimal depths required by different segmentation tasks
Solution Approach 1:
The patent segments the encoder-decoder network into multiple parallel pathways with different depth levels, where each pathway processes features at a specific depth. This segmentation allows the model to capture both shallow fine-grained features and deep semantic features simultaneously, adapting to varying task requirements while maintaining a unified trainable structure rather than requiring multiple separate models.
Data Source
AI summary
Methods, systems, and media for segmenting images are provided. In some embodiments, the method comprises: generating an aggregate U-Net comprised of a plurality of U-Nets, wherein each U-Net in the plurality of U-Nets has a different depth, wherein each U-Net is comprised of a plurality of nodes Xi,j, wherein i indicates a down-sampling layer the U-Net, and wherein j indicates a convolution layer of the U-Net; training the aggregate U-Net by: for each training sample in a group of training samples, calculating, for each node in the plurality of nodes Xi,j, a feature map xi,j, wherein xi,j is based on a convolution operation performed on a down-sampling of an output from Xi−1,j when j=0, and wherein xi,j is based on a convolution operation performed on an up-sampling operation of an output from Xi+1,j−1 when j>0; and predicting a segmentation of a test image using the trained aggregate U-Net.