Medical Image Anonymization for Privacy-Safe Data Sharing
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Solution Overview
Problem
Medical institutions face challenges in collecting medical images that significantly contribute to improving diagnosis accuracy due to limited image sources and the difficulty in sharing anonymized patient data for machine learning, leading to inefficient training and diagnosis support.
Innovation Solution
A medical image processing apparatus that receives and analyzes medical images, detects corrected analysis results, and conceals patient identification information to create anonymized data for sharing across institutions, facilitating the collection of images that enhance diagnosis accuracy through machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If medical institutions share medical images to improve machine learning accuracy, then the quantity and diversity of training data increases, but patient privacy protection becomes compromised
Solution Approach 1:
The patent extracts and removes personally identifiable information from medical images and associated data before sharing them across institutions. This extraction process separates the useful diagnostic and training content from the sensitive patient identification elements, allowing the medical images to be shared for machine learning improvement while protecting patient privacy.
Solution Approach 2:
The patent introduces an intermediary processing system that anonymizes medical images before they are shared across different medical institutions. This intermediary layer ensures that patient identifying information is removed or obscured, allowing institutions to collaborate on improving machine learning models without directly exposing patient privacy.
2Ease of manufacture
If a single medical institution accumulates medical images for machine learning, then data collection is simplified, but the diversity and quantity of images are limited
Solution Approach 1:
The patent combines medical images from multiple different medical institutions into a unified machine learning training dataset. By merging data sources across institutions while applying anonymization, the system achieves both the ease of collection from individual institutions and the diversity that comes from aggregated multi-institutional data.
Solution Approach 2:
The patent creates a universal anonymized data format and sharing mechanism that can be adopted by multiple medical institutions. This universal approach allows each institution to contribute their unique images while maintaining consistent processing standards, thereby increasing overall data diversity without complicating the collection process for individual participants.
3Reliability
If physicians manually interpret all medical images, then diagnostic accuracy can be maintained, but the workload and time consumption become excessive
Solution Approach 1:
The patent implements preliminary machine learning-based analysis of medical images before physician review. The system pre-processes images to identify potential lesions, abnormalities, or regions of interest, thereby reducing the overall workload for physicians while maintaining diagnostic accuracy through their final verification of the pre-analyzed results.
Solution Approach 2:
The patent establishes a feedback loop where physician interpretations of machine learning results are used to continuously improve the system. By incorporating physician corrections and validations back into the training dataset, the system learns from actual diagnostic cases, improving its accuracy over time and progressively reducing the burden on physicians while maintaining or enhancing diagnostic reliability.
Data Source
AI summary
Provided are a medical image processing apparatus, method, program, and a diagnosis support apparatus, method, and program that can efficiently collect medical images that have a large contribution to improving the accuracy of a diagnosis using medical images. The medical image processing apparatus includes a reception unit that receives an input of a medical image and patient information corresponding to the medical image, an analysis result acquisition unit that acquires an analysis result obtained by analyzing the medical image, a detection unit that detects whether or not the analysis result has been corrected, and a data processing unit that creates and stores data in which identification information capable of identifying a patient is concealed in a case where it is detected that the analysis result has been corrected.