Medical Image Diffusion Synthesis for Realistic Pathology Augmentation
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Solution Overview
Problem
Deep learning neural networks trained on predominantly healthy anatomical structures struggle to confidently perform inferencing tasks on medical images depicting pathological or aberrant structures due to the scarcity of such data, leading to unrealistic synthetic images with noticeable pasting artifacts.
Innovation Solution
The use of truncated reverse-diffusion processes to synthesize aberrant medical images by pasting foreign objects into medical images, followed by a truncated forward-diffusion process to reduce artifacts, and then reversing the process to create biologically plausible synthetic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If deep learning neural networks are trained on predominantly healthy anatomical structures, then the training process is simplified and data availability is improved, but the network's ability to perform inferencing tasks on pathological structures deteriorates
Solution Approach 1:
The patent applies preliminary action by generating synthetic pathological images before actual training of the inferencing network. The diffusion model pre-generates diverse pathological variants (tumors, lesions, artifacts) that are then used to augment the training dataset, enabling the network to learn from a broader range of conditions without requiring extensive collection of rare pathological cases.
Solution Approach 2:
The patent uses copying by creating synthetic copies of pathological images through the diffusion model. These generated images replicate the characteristics of real pathological structures while providing additional training examples, effectively multiplying the available training data without requiring additional physical samples or patient data.
2Adaptability or versatility
If conventional image synthesis methods are used to generate pathological images, then data diversity is improved, but image realism deteriorates due to noticeable pasting artifacts
Solution Approach 1:
The patent replaces the mechanical copy-paste system with a diffusion-based generative system. Instead of mechanically cutting and pasting foreign objects into images, the diffusion model learns the underlying data distribution and generates realistic pathological images through probabilistic sampling, substituting a sophisticated computational process for simple mechanical operations.
Solution Approach 2:
The patent applies parameter changes by adjusting the diffusion process parameters (number of diffusion steps, noise schedules, guidance scales) to optimize the balance between diversity and realism. By carefully controlling these parameters, the system generates images that maintain high realism while achieving sufficient diversity for training purposes.
3Manufacturing precision
If full reverse-diffusion process is used to generate images, then image quality is improved, but computational time and resources deteriorate
Solution Approach 1:
The patent applies partial action by using only a subset of the full diffusion steps (e.g., 1-10 steps out of typical 100-1000 steps). This truncated approach generates images of sufficient quality for training purposes without requiring the complete diffusion process, thereby reducing computational time and resources while maintaining adequate image realism.
4Quantity of substance
If voluminous training data is collected to represent various anatomical structures, then training comprehensiveness is improved, but data acquisition complexity and cost deteriorate
Solution Approach 1:
The patent uses copying to generate synthetic training data that replicates the characteristics of real medical images. The diffusion model creates numerous copies of pathological variants from limited real examples, providing voluminous training data without requiring proportionally large amounts of real patient data or complex data acquisition systems.
Solution Approach 2:
The patent applies preliminary action by pre-generating diverse pathological images before the actual training phase. This preliminary data preparation creates a comprehensive training set that covers various anatomical structures and pathologies, eliminating the need for complex real-time data acquisition during model development.
Data Source
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AI summary
Systems/techniques that facilitate aberrant image synthesis via truncated reverse-diffusion are provided. In various embodiments, a system can access a scanned medical image (e.g., 104) depicting an anatomical structure (e.g., 106) of a medical patient. In various aspects, the system can generate, via a diffusion neural network (e.g., 202) executed in a truncated reverse-diffusion process (e.g., 704) beginning at an intermediate level of noise rather than full noise, a synthetic version (e.g., 706) of the scanned medical image, wherein the synthetic version of the scanned medical image can depict the anatomical structure exhibiting a foreign object (e.g., 402).