Semantic Genesis Models for Self-Supervised Medical Imaging
Solution Overview
Problem
The challenge in medical imaging is the tedious and time-consuming process of annotating images, which requires costly, specialty-oriented expertise and can lead to misdiagnosis with potentially devastating consequences.
Innovation Solution
The development of Semantic Genesis models through self-supervised learning, which enables the generation of semantics-enriched, pre-trained 3D models without manual labeling, by employing a self-supervised learning framework that includes self-discovery, self-classification, and self-restoration of anatomical patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of medical images is performed, then diagnostic accuracy can be improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training deep learning models on large volumes of unlabeled medical images using self-supervised learning. This preliminary training enables the models to learn anatomical patterns and semantic representations before being deployed for actual diagnostic tasks, thereby reducing the need for time-consuming manual annotation while maintaining diagnostic accuracy.
Solution Approach 2:
The system implements self-service through self-supervised learning frameworks where the model automatically generates its own training signals from unlabeled medical images. The model performs self-discovery of anatomical patterns, self-classification of image regions, and self-restoration of corrupted images, eliminating dependence on manual expert annotation while improving diagnostic capabilities.
2Manufacturing precision
If manual annotation of medical images is performed, then model training quality can be improved, but expert resource requirements and cost increase
Solution Approach 1:
The system employs self-service by enabling models to automatically learn from unlabeled medical images through self-supervised learning. The model performs self-discovery of anatomical structures, self-classification of medical image regions, and self-restoration of degraded images, thereby achieving high training quality without requiring expensive expert annotation resources.
Solution Approach 2:
The system introduces an intermediary mechanism by using self-supervised learning frameworks that act as mediators between unlabeled medical images and model training. This intermediary approach generates pseudo-labels and training signals automatically, bridging the gap between unavailable expert annotations and the need for高质量 training data.
3Productivity
If self-supervised learning is used, then annotation cost and time are reduced, but model performance may deteriorate without manual labels
Solution Approach 1:
The system achieves both high productivity and model performance through self-service mechanisms. The model automatically performs self-discovery of anatomical patterns, self-classification of image regions, and self-restoration of corrupted images, generating high-quality training signals from unlabeled data that maintain diagnostic accuracy while dramatically improving annotation efficiency.
Solution Approach 2:
The system uses preliminary action by pre-training models on large volumes of unlabeled medical images using self-supervised learning before deployment. This preliminary training phase enables the model to learn robust anatomical representations that transfer effectively to downstream diagnostic tasks, ensuring high performance without manual annotation.
4Speed
If deep learning models are trained without semantic enrichment, then training speed increases, but diagnostic capability decreases
Solution Approach 1:
The system achieves both fast training and reliable diagnostic capability through self-service mechanisms. The model automatically performs self-discovery of anatomical patterns, self-classification of medical image regions, and self-restoration of degraded images, generating semantically enriched training signals that improve diagnostic capability while maintaining training speed through automated processes.
Solution Approach 2:
The system uses preliminary action by pre-training models on large volumes of unlabeled medical images using self-supervised learning before deployment. This preliminary training phase enables the model to learn robust anatomical representations that transfer effectively to downstream diagnostic tasks, ensuring both training efficiency and diagnostic reliability.
Data Source
AI summary
Described herein are means for the generation of semantic genesis models through self-supervised learning in the absence of manual labeling, in which the trained semantic genesis models are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured with means for performing a self-discovery operation which crops 2D patches or crops 3D cubes from similar patient scans received at the system as input; means for transforming each anatomical pattern represented within the cropped 2D patches or the cropped 3D cubes to generate transformed 2D anatomical patterns or transformed 3D anatomical patterns; means for performing a self-classification operation of the transformed anatomical patterns by formulating a C-way multi-class classification task for representation learning; means for performing a self-restoration operation by recovering original anatomical patterns from the transformed 2D patches or transformed 3D cubes having transformed anatomical patterns embedded therein to learn different sets of visual representation; and means for providing a semantics-enriched pre-trained AI model having a trained encoder-decoder structure with skip connections in between based on the performance of the self-discovery operation, the self-classification operation, and the self-restoration operation. Other related embodiments are disclosed.