GAN-Generated Data for Digital Pathology Similarity Learning
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Solution Overview
Problem
Existing machine learning models struggle to adapt from natural image domains to pathology images for similarity learning due to the difficulty in acquiring well-annotated datasets and the high human bias involved in manual annotation of histopathology images.
Innovation Solution
A system and method for generating training image data using a generative adversarial network (GAN) to create synthetic histopathology images, which are classified as similar or dissimilar, and incorporating coarse annotations to automate the annotation process, thereby creating a robust training dataset for similarity learning in histopathology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of histopathology images is used to create training datasets, then the accuracy of similarity learning is improved, but the time required and human bias increase significantly
Solution Approach 1:
The patent uses GANs to generate synthetic histopathology images that replicate real images. These synthetic images serve as copies that can be automatically annotated, replacing the time-consuming manual annotation process while maintaining the quality needed for training similarity learning models.
Solution Approach 2:
The system uses existing real histopathology images to train GANs, which then automatically generate synthetic images for training. This self-service approach eliminates the need for manual annotation by having the system generate its own training data from existing resources.
2Measurement precision
If manual annotation of histopathology images is used to create training datasets, then the accuracy of similarity learning is improved, but human bias in annotation increases
Solution Approach 1:
By creating synthetic copies of histopathology images through GANs, the system eliminates human annotators entirely. The synthetic images are generated algorithmically based on real image data, removing human bias from the annotation process while preserving the essential features needed for accurate similarity learning.
Solution Approach 2:
The patent replaces the mechanical process of manual human annotation with an automated computational system. GANs generate synthetic images that can be processed by algorithms, substituting human cognitive judgment with machine-based generation that is free from human bias.
3Productivity
If GANs are used to generate synthetic histopathology images, then the speed of training data generation is improved, but the complexity of the system increases
Solution Approach 1:
The GAN framework serves multiple functions: it generates synthetic images, provides automatic annotations, and creates training datasets. This multi-functionality consolidates several operations into a single system, improving productivity while managing complexity through unified architecture.
Solution Approach 2:
The patent implements feedback mechanisms where synthetic images are evaluated and used to refine the GAN model. This iterative feedback process optimizes the generation quality while providing a structured approach to managing system complexity through controlled refinement cycles.
Data Source
AI summary
Systems and methods for similarity learning in digital pathology are provided. In one aspect, an apparatus for generating training image data includes a hardware memory configured to store executable instructions and a hardware processor in communication with the hardware memory, wherein the executable instructions, when executed by the processor, cause the processor to obtain a plurality of histopathology images, classify two or more of the histopathology images as similar or dissimilar, and create a dataset of training image data including the classified histopathology images.