Medical Object Detection via Entity Embedding Consistency
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Solution Overview
Problem
Current medical image analysis techniques, such as those using RetinaNet, face challenges in accurately detecting medical conditions like cancer from multiple views of the same organ, as they rely on conventional classification and localization without effectively utilizing information from different views to enhance sensitivity and specificity.
Innovation Solution
The introduction of a penalty term in the loss function of a machine learning model, which encourages identical entity embeddings for the same physical entity across multiple views and discourages them for different entities, combined with the use of identity vectors to improve the model's ability to identify and classify medical issues in multi-view medical images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional classification and localization are used without penalty terms, then the model can process multiple views efficiently, but the detection specificity and sensitivity are insufficient
Solution Approach 1:
The patent introduces a penalty term in the loss function that provides feedback to the model during training. This penalty term calculates the distance between entity embeddings from different views and feeds this information back to the model, encouraging it to produce identical embeddings for the same physical entity across multiple views. This feedback mechanism directly improves detection specificity and sensitivity by ensuring consistent entity identification across views.
Solution Approach 2:
The patent modifies the loss function by adding a penalty term that changes the optimization parameters. The penalty term incorporates the distance between entity embeddings as a new parameter to be minimized, alongside the conventional classification and localization losses. This parameter change transforms the model's optimization objective to include view consistency, thereby improving reliability without fundamentally changing the model architecture.
2Measurement precision
If identity vectors are added to enforce consistency across views, then entity identification accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by creating identity vectors for each detected entity during the training process. These identity vectors are generated in advance as part of the training pipeline and are used to compute the penalty term. By preparing these vectors during training rather than during inference, the system achieves high entity identification accuracy while minimizing additional computational complexity during deployment.
Solution Approach 2:
The identity vectors serve as intermediaries between the multiple views of the same physical entity. Instead of directly comparing all possible entity pairs across views, the model uses identity vectors as mediator representations. Each identity vector encapsulates the characteristics of a physical entity, and the penalty term operates on these vectors to enforce consistency. This intermediary approach reduces computational complexity compared to direct pairwise comparison.
3Reliability
If the model is trained to recognize same physical entities across multiple views, then detection accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action by introducing the penalty term selectively during the training process rather than requiring complete retraining of the entire model. The penalty term is added to the existing loss function, allowing the model to leverage pre-trained features while only optimizing the additional consistency constraint. This approach achieves improved detection accuracy across multiple views without requiring excessive training time or computational resources.
Data Source
AI summary
An approach for improving object detection performance by using bilateral organ information disclosed. The approach comprises introducing a penalty term to an object localization model. The penalty term encourages the model to identify same physical regions (actual same physical location in the patient's body) in multiple images, which create an image of the same organ. The approach includes a sub-component of the model that can output an entity embedding, in additional to the conventional classification and localization prediction. During optimization process, similar entity embedding for the same logical entity are encouraged, and similar entity embedding for different logical entities are discouraged.