Mixed-Format Labels for MR Pathology Detection
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Solution Overview
Problem
Current machine learning models for pathology detection in medical imaging face challenges in generalizing across institutions and demographics due to differences in imaging protocols and label formats, leading to inefficiencies in combining mixed-format labeled datasets for training.
Innovation Solution
A multi-stage machine learning architecture that combines positional and categorical labels to enhance model attention and performance, using a UNet network with morphological peak-finding for defect localization and a convolutional network for classification, allowing for the efficient training and inference on diverse datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different labeling schemes and formats are used for training data, then the adaptability and versatility of the model improves, but the device complexity and difficulty of combining datasets increases
Solution Approach 1:
The patent segments the training data into distinct format categories (categorical labels, positional labels, bounding boxes, point landmarks) and processes each segment through dedicated processing pipelines before integration. This segmentation allows the system to handle diverse label formats without requiring a complete redesign of the processing architecture, thereby maintaining model adaptability while managing data processing complexity.
Solution Approach 2:
The patent introduces an intermediary layer that converts various label formats (categorical, positional, bounding boxes, point landmarks) into a unified representation that the neural network can process. This intermediary processing step acts as a mediator between diverse data sources and the model, enabling the system to accommodate multiple labeling schemes without directly increasing the complexity of the core model architecture.
2Measurement precision
If positional label information is augmented to categorical labels, then the measurement precision and detection accuracy improves, but the loss of time and processing duration increases
Solution Approach 1:
The patent performs preliminary processing of positional label information during the data preparation phase, converting coordinate data and spatial information into pre-computed features before training begins. This preliminary action ensures that when the model trains, the augmented information is already in an optimized format, reducing the computational burden during training and minimizing the time penalty associated with processing complex positional data.
Solution Approach 2:
The patent selectively augments categorical labels with positional information based on the specific requirements of each pathology detection task. Rather than universally applying complex positional augmentation to all datasets, the system applies partial augmentation only where it provides measurable accuracy improvements, thereby balancing the trade-off between detection precision and training time investment.
3Productivity
If a multi-stage machine learning architecture is used, then the productivity and detection performance improves, but the device complexity increases
Solution Approach 1:
The patent segments the pathology detection task into multiple specialized stages: a first neural network stage that processes categorical labels and a second neural network stage that processes positional labels and generates refined predictions. This segmentation allows each stage to be optimized for its specific function, improving overall detection throughput while containing architecture complexity through modular design.
Solution Approach 2:
The patent designs the multi-stage architecture with universal components that can handle multiple label formats across different stages. The neural networks are configured to accept various input types (categorical labels, positional coordinates, bounding boxes) and produce unified output formats, thereby improving detection performance across diverse datasets without proportionally increasing architectural complexity.
Data Source
AI summary
Described are systems, techniques, and processes for pathology detection in radiological images. A process can include obtaining a radiological image corresponding to an imaged anatomical area. Based on processing the radiological image using a semantic segmentation neural network, a target map can be generated corresponding to a plurality of candidate anatomical defect locations within the cropped radiological image. At least one volume of interest (VOI) can be generated centered around a particular candidate anatomical defect location within the cropped radiological image. A classification neural network can be used to classify the particular candidate anatomical defect location within the cropped radiological image, wherein classifying the particular candidate anatomical defect location includes determining a pathology associated with the particular candidate anatomical defect location.