Neural Network Noise Robustness for Radiology Image Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning systems for radiology image classification are hindered by noise in images, leading to decreased accuracy, especially when images are captured with non-digital methods like cell phone cameras, introducing extraneous noise such as optical flaws, glare, and the Moiré effect.
Innovation Solution
A system and method that generates robust neural networks trained using augmented data sets, which include synthetic noise introduction, allowing the networks to operate effectively on noisy images, and includes a specialist neural network trained to provide investigation recommendations like heat maps and estimated diagnoses, deployable on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If images are captured with non-digital methods like cell phone cameras, then accessibility and ease of operation are improved, but noise and measurement precision deteriorate
Solution Approach 1:
The system performs preliminary actions by capturing multiple images of the same radiology film and preprocessing them before classification. This includes aligning images, reducing duplicates, and preparing augmented training data with synthetic noise patterns that mimic real-world capture conditions, thereby improving robustness before actual classification occurs
Solution Approach 2:
The patent introduces an intermediary processing layer between image capture and classification. This includes using multiple camera captures as intermediaries to create a composite view, and employing preprocessing steps like noise filtering and image alignment that act as intermediaries to bridge the gap between noisy captured images and the classification algorithm
2Reliability
If neural networks are trained with augmented data including synthetic noise, then robustness to noise is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-generating and storing augmented training data with various synthetic noise patterns (optical flaws, glare, Moiré effects) before actual model training. This preparation work is done in advance so that during deployment, the model can directly apply learned noise robustness without real-time processing delays
Solution Approach 2:
The patent applies partial action by selectively applying different noise augmentation techniques to different training samples rather than uniformly to all data. This allows the model to learn from diverse noise conditions while avoiding over-training on any single noise type, optimizing the balance between robustness and training efficiency
3Measurement precision
If multiple images are captured and processed to reduce noise, then image quality is improved, but processing complexity and time increase
Solution Approach 1:
The system segments the image processing task into distinct stages: capture phase (multiple images), preprocessing phase (alignment and deduplication), and classification phase. This segmentation allows each stage to be optimized independently, reducing overall complexity while maintaining quality improvements from multiple captures
Data Source
AI summary
Systems and methods for radiology image classification from noisy images in accordance with embodiments of the invention are illustrated. One embodiment includes noisy image classification device, including a processor, camera circuitry, and a memory containing a noisy image classification application, where the noisy image classification application directs the processor to obtain image data describing a first image taken of a second image using the camera circuitry, where the second image was produced by a medical imaging device, and where the first image is a noisy version of the second image, classify the image data using a neural network trained to be robust to noise, generate an investigation recommendation based on the classification, and provide the investigation recommendation via a display.