Cloud Feedback Loop for Medical Image Classification
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Solution Overview
Problem
The growing volume of healthcare data, particularly in the form of images, poses a challenge in clinical diagnosis, as existing technologies lack efficient methods for processing and utilizing this data to provide accurate and scalable solutions for image recognition and feedback-driven training.
Innovation Solution
A cloud-based infrastructure that implements a feedback-driven training loop for image recognition, utilizing expert annotations to train classification models, update them incrementally, and provide a scalable system for executing tasks against an analytics pipeline, including a machine learning and analytics pipeline, application middleware, and extensible document and media storage platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are trained on large volumes of healthcare image data, then classification accuracy and diagnostic performance are improved, but computational infrastructure requirements and system complexity increase
Solution Approach 1:
The patent introduces a cloud-based intermediary platform that mediates between healthcare providers and complex machine learning computations. This cloud infrastructure handles the computational burden externally, allowing local systems to benefit from advanced AI capabilities without maintaining complex computational infrastructure themselves.
Solution Approach 2:
The patent replaces traditional local mechanical computing systems with cloud-based virtualized computing resources. This substitution allows machine learning algorithms to be executed on remote servers with scalable computational power, eliminating the need for expensive local hardware investments while maintaining high classification accuracy.
2Ease of manufacture
If cloud computing is adopted for healthcare data processing, then cost-effectiveness and accessibility are improved, but patient data privacy and regulatory compliance challenges increase
Solution Approach 1:
The patent segments data processing operations into distinct components: sensitive patient data remains localized or is encrypted in transit, while only anonymized feature extracts are sent to the cloud for processing. This segmentation maintains cost-effectiveness through cloud computing while mitigating privacy risks by minimizing data exposure.
Solution Approach 2:
The patent introduces data encryption and anonymization as intermediary layers between healthcare data and cloud processing systems. These intermediary mechanisms preserve the utility of data for machine learning while protecting patient privacy and ensuring regulatory compliance during cloud-based processing.
3Reliability
If feedback-driven training loops are implemented to continuously improve models, then diagnostic performance is improved, but training time and computational resources are increased
Solution Approach 1:
The patent implements partial feedback-driven training by selectively updating models only with feedback from uncertain or edge-case predictions, rather than continuously retraining on all data. This partial action approach maintains diagnostic performance improvement while significantly reducing training time and computational resource consumption.
Solution Approach 2:
The patent establishes a continuous feedback loop where model predictions are continuously evaluated and fed back into the training process. This continuous action ensures diagnostic performance is constantly improved through incremental learning from new data and feedback, balancing performance gains with efficient training time utilization.
4Productivity
If scalable cloud infrastructure is deployed to handle growing healthcare data volumes, then productivity and data processing capability are improved, but system complexity and implementation challenges increase
Solution Approach 1:
The patent designs a universal cloud-based platform that handles multiple functions: data storage, machine learning training, model deployment, and diagnostic support. This multi-functional approach consolidates what would otherwise require separate complex systems into a single scalable platform, improving productivity while managing system complexity through integration.
Data Source
AI summary
A method for a cloud-based feedback-driven image training and recognition includes receiving a set of expert annotations of a plurality of training images of a predetermined subject matter, wherein the expert annotations include a clinical diagnosis for each image or region of interest in an image, training one or more classification models from the set of expert annotations, testing the one or more classification models on a plurality of test images that are different from the training images, wherein each classification model yields a clinical diagnosis for each image and a confidence score for that diagnosis, and receiving expert classification result feedback regarding the clinical diagnosis for each image and a confidence score yielded by each classification model.