Source Model Generation for Medical Imaging Transfer Learning
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Solution Overview
Problem
Current methods for transfer learning in medical imaging face challenges due to the difficulty in generating suitable source models for application-specific tasks, particularly in transitioning from natural images to medical images, and the resource-intensity of annotating medical images, which hampers the accuracy and efficiency of medical image analysis.
Innovation Solution
The development of systems and methods for generating source models using encoder-decoder networks that learn anatomical patterns from medical images through transformations such as non-linear intensity changes, local pixel shuffling, out-painting, and in-painting, allowing for self-supervised training without annotated datasets, enabling the creation of robust source models for application-specific models in medical imaging tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If source models are generated using natural images for transfer learning to medical image analysis, then the model can be trained without annotated medical images, but the model performance and accuracy deteriorate due to domain mismatch between natural and medical images
Solution Approach 1:
The patent introduces an intermediary domain of synthesized medical images generated by GANs that bridges the gap between natural images and real medical images. The transfer learning process flows from natural images to synthesized medical images (intermediary) and then to real medical images, rather than direct transfer. This intermediary domain adapts the feature extractor to medical image characteristics while avoiding the need for annotated real medical images during pre-training.
2Measurement precision
If annotated medical images are used to train source models for transfer learning, then model accuracy improves, but the resource intensity and time required for annotation increases significantly
Solution Approach 1:
The system uses self-supervised learning where the GAN framework automatically generates labeled training data from unlabeled real medical images. The generator creates synthetic images with corresponding labels without human annotation, and the discriminator and generator networks train each other in an adversarial process. This self-service mechanism eliminates the need for manual annotation while still producing accurate source models for transfer learning.
3Productivity
If 2D source models are used for medical image analysis, then the model training is simpler and faster, but the model performance deteriorates when applied to 3D medical imaging modalities like CT and MRI
Solution Approach 1:
The patent transitions from 2D to 3D by using 3D GANs to generate volumetric medical images that match the dimensional structure of CT and MRI scans. The synthesized images preserve 3D spatial relationships and anatomical structures, allowing the source model to learn features appropriate for 3D medical imaging while maintaining training efficiency through the automated synthesis process.
Data Source
AI summary
Described herein are means for generating source models for transfer learning to application specific models used in the processing of medical imaging. In some embodiments, the method comprises: identifying a group of training samples, wherein each training sample in the group of training samples includes an image; for each training sample in the group of training samples: identifying an original patch of the image corresponding to the training sample; identifying one or more transformations to be applied to the original patch; generating a transformed patch by applying the one or more transformations to the identified patch; and training an encoder-decoder network using a group of transformed patches corresponding to the group of training samples, wherein the encoder-decoder network is trained to generate an approximation of the original patch from a corresponding transformed patch, and wherein the encoder-decoder network is trained to minimize a loss function that indicates a difference between the generated approximation of the original patch and the original patch. The source models significantly enhance the transfer learning performance for many medical imaging tasks including, but not limited to, disease/organ detection, classification, and segmentation. Other related embodiments are disclosed.