Implicit Neural Representations for Medical Image Dataset Generation
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Solution Overview
Problem
Existing methods for generating Neural Datasets of medical images for Machine Learning tasks are inefficient, requiring significant time and processing resources, and lack understanding of how hyperparameters impact INR quality.
Innovation Solution
A computer-implemented method for generating a Neural Dataset of medical images by obtaining a dataset of medical images, a Neural Field, and seeds for initiating the Neural Field, then generating Implicit Neural Representations (INRs) in parallel for a subset of the dataset, optimizing for efficient conversion and representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional array representations are used for medical images, then the data can be processed on standard grids, but the continuous nature of the underlying signal is lost and memory/compute efficiency is reduced
Solution Approach 1:
The patent replaces traditional array-based mechanical grid structures with Implicit Neural Representations that use continuous mathematical functions. This substitution allows the system to maintain signal accuracy while reducing memory requirements, as the INR stores only the function parameters rather than discrete pixel values for every possible resolution.
Solution Approach 2:
The patent changes the fundamental parameter representation from discrete array indices to continuous coordinate-space parameters. By representing images as functions f(x,y) that map coordinates to RGB values, the system achieves both continuous signal representation and computational efficiency through parameter-based storage rather than full-array storage.
2Measurement precision
If INRs are trained for each data sample from scratch, then each signal can be accurately represented, but the operation becomes prohibitively expensive for large datasets
Solution Approach 1:
The patent performs preliminary action by pre-training a shared backbone neural network on the entire dataset before generating individual INRs. This preliminary training establishes common features and representations that can be rapidly fine-tuned for each sample, avoiding the need to train from scratch and significantly improving dataset generation speed.
Solution Approach 2:
The patent segments the training process into two distinct phases: (1) shared backbone training that learns common representations for all samples, and (2) individual INR fitting that adapts to specific samples. This segmentation allows efficient reuse of the shared learned features while maintaining sample-specific accuracy.
3Reliability
If the total potential Neural Dataset is generated with all seeds, then comprehensive coverage is achieved, but the computational resources and time required become excessive
Solution Approach 1:
The patent applies partial action by generating only a subset of the total potential Neural Dataset rather than exhaustively training all possible seed combinations. This selective approach achieves sufficient dataset comprehensiveness for reliable ML tasks while avoiding the excessive computational cost of complete dataset generation.
Solution Approach 2:
The patent uses preliminary action by pre-selecting a strategically chosen subset of seeds that provide adequate diversity and coverage. This preliminary selection allows the system to achieve reliable dataset comprehensiveness with significantly reduced computational resources compared to exhaustive generation.
Data Source
AI summary
A computer-implemented method for generating a neural dataset of medical images for use in a Machine Learning (ML) task. The method comprises obtaining a dataset of medical images, a neural field, and seeds for initiating the neural field. A size of a first subset for the neural dataset is obtained, smaller than a total potential neural dataset comprising one or more Implicit Neural Representations (INRs) generated from the neural field initiated with each seed for each medical image. Additional INRs are generated for the first subset by fitting the neural field, initiated with a seed, to a medical image. The INRs in the first subset are output as the neural dataset. Generating INRs for the first subset involves parallel generation of multiple INRs of the first subset on a single processing apparatus.