Cognition Integrator Scripting for Dynamic Mammogram Analysis
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Solution Overview
Problem
Current computer-aided detection (CAD) systems for mammograms face challenges in accurately distinguishing cancerous mass lesions and micro-calcifications while maintaining a low false positive detection rate, as they rely on static rules-based selection methods that do not adapt to the characteristics of the digital image.
Innovation Solution
The Cognition Program employs a computer-implemented network structure that links pixel values to objects in a data network, allowing for dynamic analysis and detection of target objects by specifying classes and process steps through a novel scripting language, integrating pixel data with metadata to adaptively identify cancerous regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rules-based selection methods with fixed thresholds are used for detecting cancerous regions, then the detection process is simple and fast, but the false positive detection rate increases and the system cannot adapt to different image characteristics
Solution Approach 1:
The patent transforms the static rules-based detection system into a dynamic cognitive system that adapts to different image characteristics. The cognition network dynamically adjusts its parameters and rules based on the specific features of each mammogram, allowing the system to maintain high detection speed while reducing false positives by adapting to the unique characteristics of each case rather than applying fixed thresholds universally.
Solution Approach 2:
The system changes detection parameters dynamically based on image characteristics. Instead of using fixed thresholds and rules, the cognition network adjusts sensitivity, specificity, and detection criteria according to the specific features of each mammogram, enabling the system to optimize between detection speed and accuracy for each individual case.
2Measurement precision
If the probability threshold for detecting cancerous regions is lowered to increase sensitivity, then more cancerous regions are detected, but the false positive detection rate increases significantly
Solution Approach 1:
The cognition network dynamically adjusts detection thresholds and parameters based on the specific characteristics of each detected region and the overall image context. This allows the system to maintain high sensitivity for detecting cancerous regions while adapting the threshold locally to minimize false positives, rather than applying a single global threshold that must balance both concerns.
Solution Approach 2:
The system applies different detection criteria and probability thresholds to different regions of the mammogram based on their local characteristics. Areas with features more suggestive of malignancy receive different threshold treatment than benign-appearing areas, allowing high sensitivity where needed while maintaining low false positive rates in other regions.
3Ease of manufacture
If static rules-based methods are used for analyzing mammograms, then the system is easy to implement and interpret, but it cannot adapt to the varying characteristics of different digital images
Solution Approach 1:
The patent implements a dynamic cognition network that automatically adapts to different image characteristics while maintaining a structured framework. The system uses predefined cognitive patterns and rules as a foundation (maintaining implementability) but allows these rules to be dynamically adjusted and weighted based on the specific features of each mammogram (achieving adaptability).
Solution Approach 2:
The cognition network performs self-adjustment and self-optimization based on the input image characteristics. The system automatically learns and adapts its detection parameters and rules from the data, reducing the need for manual configuration and interpretation while maintaining adaptability to different image types and characteristics.
Data Source
AI summary
In a specification mode, a user specifies classes of a class network and process steps of a process hierarchy using a novel scripting language. The classes describe what the user expects to find in digital images. The process hierarchy describes how the digital images are to be analyzed. Each process step includes an algorithm and a domain that specifies the classes on which the algorithm is to operate. A Cognition Program acquires table data that includes pixel values of the digital images, as well as metadata relating to the digital images. In an execution mode, the Cognition Program generates a data network in which pixel values are linked to objects, and objects are categorized as belonging to classes. The process steps, classes and objects are linked to each other in a computer-implemented network structure in a manner that enables the Cognition Program to detect target objects in the digital images.