Medical Image Pre-Training With Discrete Anatomical Concepts
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Solution Overview
Problem
Conventional self-supervised learning (SSL) methods for pre-training fail to capture fine-grained concepts in medical images, such as anatomical structures, leading to poor performance in localized tasks like pathology detection and Out-of-Distribution (OOD) detection, and lack of interpretability and personalization in medical imaging.
Innovation Solution
A method for pre-training a principal encoder and concept head using a neural network system that processes medical images to obtain discretized anatomical concepts and continuous styles, employing reconstruction losses without labeled data, to enhance image characterization and interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional SSL methods use single-vector embeddings to represent medical images, then the model can process images efficiently, but it fails to capture fine-grained concepts such as anatomical structures
Solution Approach 1:
The patent segments the single-vector embedding into multiple discrete concept vectors, each representing a specific anatomical structure or semantic concept. This segmentation allows the model to capture fine-grained concepts by assigning separate vectors to different anatomical structures (e.g., left ventricle, right ventricle, atria) while maintaining overall image representation through the collection of these segmented vectors.
2Reliability
If SSL methods impose similarity constraints on augmented views to learn invariant representations, then the model achieves robustness to transformations, but it merges distinct concepts and their associated styles
Solution Approach 1:
The patent applies local quality by allowing different concepts to have different transformation behaviors. Instead of enforcing uniform similarity constraints across all concepts, the method permits each discrete concept vector to maintain its unique characteristics and transformation patterns. This enables the model to achieve robustness for each concept individually while preserving concept differentiation, as each concept can be transformed and compared separately rather than being merged into a single aggregated representation.
3Adaptability or versatility
If conventional SSL pre-training focuses on image-level embeddings, then the model learns global image characteristics, but it cannot perform well on localized tasks such as pathology detection
Solution Approach 1:
The patent segments the image representation into discrete concept vectors corresponding to specific anatomical structures, enabling localized task performance. Each concept vector can be independently processed and analyzed for localized pathologies or abnormalities within specific anatomical regions, while the collection of all concept vectors maintains the global image context. This segmentation approach allows the model to adapt to localized tasks without losing fine-grained anatomical information.
4Ease of manufacture
If SSL methods use vast amounts of unlabelled data for pre-training, then the model learns intrinsic patterns, but it lacks interpretability and personalization for medical imaging applications
Solution Approach 1:
The patent introduces discrete concept vectors as intermediary representations between the raw image data and the final predictions. These concept vectors serve as interpretable mediators that explicitly represent anatomical structures and semantic concepts, making the model's decision-making process transparent. The concept vectors act as a bridge that connects unlabelled data learning with medical domain knowledge, enabling both interpretability and personalization while still utilizing unlabelled data for pre-training.
Data Source
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AI summary
The invention relates to a technique for pre-training a principal encoder and a concept head. A method comprises receiving, at a principal encoder (302), a medical image (502) and processing it for obtaining a principal latent representation (504), which is provided to a concept head (306) and to a style head (310) to obtain a first vector of discretized anatomical concepts and an associated further first vector of continuous styles per discretized anatomical concept in the medical image (502), respectively. An auxiliary feature decoder determines, based on the obtained first vector of discretized anatomical concepts, an auxiliary latent representation (10), based on which an auxiliary image decoder (316) performs a reconstruction (512) of the medical image (502). The principal encoder (302) and concept head (306) are pre-trained based a reconstruction loss between the received medical image (502) and the first reconstruction (512).