Weakly-Supervised Neural Network for Medical Object Detection
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Solution Overview
Problem
Current machine learning models require extensive manual annotation of training data for accurate object detection, particularly in medical imaging, which is time-consuming and costly, leading to potential inaccuracies in disease diagnosis.
Innovation Solution
A weakly-supervised attention-driven deep learning model that leverages encoded information from medical reports to improve object localization and characterization, utilizing both image and textual data to reduce the need for extensive annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive manual annotation of training data is performed to improve object detection accuracy, then detection precision improves, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary action by using weakly-supervised learning to pre-train the neural network with minimally annotated data before fine-tuning. Medical reports and image data are pre-processed to extract relevant features and relationships, reducing the need for extensive manual annotation later in the process.
Solution Approach 2:
The patent introduces an intermediary approach by using weakly-supervised learning as a bridge between fully supervised and unsupervised learning. This intermediary method allows the system to leverage partially annotated data and medical report text to guide the training process, reducing reliance on extensive manual annotations while maintaining detection accuracy.
2Measurement precision
If extensive manual annotation of training data is performed to improve object detection accuracy, then detection precision improves, but cost increases significantly
Solution Approach 1:
The system applies partial action by using only the minimum necessary annotation effort to achieve effective training. Instead of fully annotating all training data, the method uses partial annotations combined with weakly-supervised learning signals from medical reports to achieve comparable or superior performance with reduced cost.
Solution Approach 2:
The system enables self-service by allowing the neural network to learn from the structure and content of medical reports automatically. The weakly-supervised learning framework allows the model to extract useful training signals from unannotated or minimally annotated data, reducing the need for expensive manual annotation services.
3Productivity
If extensive manual annotation is reduced to save time and cost, then annotation efficiency improves, but training data quality may deteriorate
Solution Approach 1:
The patent applies universality by designing a weakly-supervised learning framework that can effectively utilize multiple types of data sources including minimally annotated images, full-text medical reports, and structured report elements. This multi-functional approach allows the system to maintain training data quality by leveraging diverse information sources rather than relying solely on extensive manual annotations.
Solution Approach 2:
The system changes parameters by transforming the training approach from requiring high-quantity manually annotated data to using lower-quantity annotations combined with weakly-supervised signals. By changing the learning paradigm and data utilization parameters, the system maintains training quality while improving annotation efficiency.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to detect one or more objects in one or more images. In at least one embodiment, one or more neural networks can be used to detect one or more objects in one or more images based, at least in part, on textual descriptions of the one or more objects.