Siamese Network for Malignancy Detection in Anatomical Image Pairs
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Solution Overview
Problem
Current methods for detecting malignancy in medical images rely on manual comparison of anatomically corresponding images, which is challenging due to asymmetry and natural changes over time, making it difficult to distinguish between normal and abnormal differences.
Innovation Solution
A computer-implemented method using a Siamese network architecture to analyze pairs of anatomical images, determining if they are from the same patient by learning a statistical distance metric, and generating an indication of likelihood of malignancy when the distance exceeds a threshold, thereby automating the detection of anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of anatomical images is used, then radiologists can review images, but the process is time-consuming and difficult due to asymmetry and natural changes
Solution Approach 1:
The patent replaces manual radiological review with an automated deep learning system that uses Siamese neural networks to compare anatomical images. The system automatically extracts features, computes statistical distances, and identifies malignancies without human intervention, thereby reducing review time while maintaining detection accuracy through sophisticated algorithms.
Solution Approach 2:
The patent transforms the manual comparison process into automated parameter-based analysis. By converting image data into feature vectors and computing statistical distances (parameters), the system objectively quantifies differences between anatomical structures, replacing subjective manual assessment with measurable, reproducible parameters that can be processed automatically.
2Productivity
If automated tools are developed to aid radiologists, then image review efficiency improves, but false positives may occur due to natural variations
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously learns from labeled data and adjusts its feature extraction and distance computation algorithms. This feedback loop allows the model to refine its understanding of normal anatomical variations over time, reducing false positives while maintaining high productivity in image review.
Solution Approach 2:
The system dynamically adjusts statistical distance thresholds and feature weighting parameters based on training data and clinical feedback. By optimizing these parameters, the system achieves both high productivity in processing large numbers of images and improved reliability through adaptive thresholding that accounts for patient-specific variations and anatomical norms.
3Measurement precision
If deep learning models are trained to detect malignancy, then detection accuracy improves, but the model may confuse natural variations with malignancy
Solution Approach 1:
The patent segments the complex task of malignancy detection into distinct functional components: feature extraction, statistical distance computation, and classification decision. This segmentation allows each component to be optimized independently, managing overall model complexity while maintaining high detection accuracy through specialized sub-functions.
Solution Approach 2:
The system manages model complexity by focusing on key statistical parameters (mean, variance, distance metrics) rather than attempting to model every possible anatomical variation. By changing parameters such as feature selection criteria and distance thresholds, the system achieves high accuracy without requiring excessively complex architectural models.
Data Source
AI summary
There is provided a computer implemented method for detection of likelihood of malignancy in an anatomical image of a patient for planning treatment thereof, comprising: receiving a pair of images that are either of a same patient or from two different patients, wherein the pair of images comprises anatomically corresponding anatomical images each depicting internal anatomical structures of the patient, feeding the pair of images into a model, outputting by the model, an indication of whether the pair of images are of a same patient or not, and generating an indication of likelihood of malignancy when the model wrongly outputs that the pair of images are not of the same patient, when in fact the pair of images are of the same patient, wherein treatment of the patient is planned according to the indication of likelihood of malignancy.