Segmentation Network Training via User-Guided Importance Sampling
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Solution Overview
Problem
Current image segmentation technologies, such as convolutional neural networks (CNNs), face challenges in efficiently training segmentation networks due to the opacity of traditional backpropagation processes, making it difficult for users to guide the network towards a desired state without a large volume of data, especially when ground truth supervisor data is scarce.
Innovation Solution
The method involves modifying the training procedure for segmentation networks using user-provided selections, which can be made through digital interfaces, to focus the training on specific aspects of the segmentation task, allowing users to improve network performance by emphasizing important areas or correcting errors, and incorporating alternative algorithmic systems to provide initial segmentations or refine them iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional backpropagation training is used with large volume of data, then network training completeness is improved, but training efficiency and user guidance capability deteriorate
Solution Approach 1:
The patent segments the training process into two distinct phases: (1) initial automated backpropagation training to establish baseline network capabilities, and (2) user-guided importance sampling training to efficiently refine specific aspects. This segmentation allows the system to achieve comprehensive training without requiring continuously large datasets, as the user guidance focuses computational resources on most needed improvements.
Solution Approach 2:
The patent implements a feedback mechanism where user selections from segmentation outputs are fed back into the training process. Users review generated segmentations and selectively indicate areas needing improvement, which then guides the next iteration of training. This closed-loop feedback system enables targeted refinement without requiring brute-force data volume increases.
2Measurement precision
If user guidance is incorporated into training, then segmentation accuracy in specific areas is improved, but training process complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the network generates initial segmentations that users can directly review and provide feedback on. This eliminates the need for complex manual annotation processes, as users work with pre-generated outputs rather than creating training data from scratch. The automated generation of initial segmentations simplifies the user's task while maintaining high accuracy potential.
Solution Approach 2:
The system performs preliminary automated segmentation before user review, preparing quality baseline outputs that users can then refine through selective feedback. This preliminary action reduces the complexity of user interaction, as users work with already-processed images rather than raw data, and can focus their guidance on specific areas needing improvement rather than entire images.
3Measurement precision
If ground truth supervisor data is used extensively, then training accuracy is improved, but data requirements and storage needs worsen
Solution Approach 1:
The patent introduces user selections as an intermediary between automated segmentation and ground truth supervision. Instead of relying exclusively on extensive ground truth data, the system uses user feedback on generated segmentations as a intermediate training signal. This intermediary approach maintains training accuracy while dramatically reducing ground truth data requirements, as users provide guidance only on problematic areas rather than annotating entire datasets.
Solution Approach 2:
The system dynamically changes the training parameter focus based on user feedback. Rather than using fixed ground truth supervision across all training data, the system adjusts which regions receive supervisory signals based on user selections. This parameter change strategy maintains accuracy in critical areas while minimizing overall data volume requirements by concentrating supervision where most needed.
Data Source
AI summary
Methods and systems regarding importance sampling for the modification of a training procedure used to train a segmentation network are disclosed herein. A disclosed method includes segmenting an image using a trainable directed graph to generate a segmentation, displaying the segmentation, receiving a first selection directed to the segmentation, and modifying a training procedure for the trainable directed graph using the first selection. In a more specific method, the training procedure alters a set of trainable values associated with the trainable directed graph based on a delta between the segmentation and a ground truth segmentation, the first selection is spatially indicative with respect to the segmentation, and the delta is calculated based on the first selection.