Brain Imaging Modality Classifier Using Low-Data Feature Clustering
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Solution Overview
Problem
Existing imaging modalities face challenges in accurately distinguishing between different imaging modalities, particularly with limited or scarce training data, leading to inefficient image data organization and retrieval.
Innovation Solution
A learning classifier based on triplet ranking networks is used to capture relevant imaging features by projecting image data into a lower dimensional space and applying clustering techniques to classify imaging modalities, even with limited training examples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Convolutional Neural Networks are used to classify imaging modalities, then classification accuracy can be improved, but large volumes of annotated data are required which are difficult to obtain for novel imaging biomarkers or rare modalities
Solution Approach 1:
The patent applies transfer learning by pre-training the CNN on ImageNet (a large-scale dataset) before fine-tuning on the target imaging modality classification task. This preliminary action on a different but related task allows the model to learn generalizable features that can be transferred to the specific medical imaging classification problem, reducing the need for large volumes of annotated medical imaging data
Solution Approach 2:
The patent modifies the standard CNN architecture by freezing certain layers during transfer learning and only training the final classification layers on the target task. This parameter change in the training strategy allows the model to leverage pre-learned features while adapting to the specific characteristics of medical imaging modalities with limited data
2Loss of information
If traditional clustering techniques are applied to high-dimensional image data, then comprehensive feature analysis can be achieved, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent extracts the most discriminative features from high-dimensional image data by using the CNN to generate compact feature embeddings. Instead of applying clustering directly to the full high-dimensional image data, the method extracts essential features that capture the most important variations between imaging modalities, reducing computational complexity while preserving the information needed for accurate classification
Solution Approach 2:
The patent transforms the high-dimensional image data into a lower-dimensional embedding space using the CNN's feature extraction capability. This dimensionality change projects the data into a space where clustering can be performed more efficiently, maintaining the essential structure and relationships between different imaging modalities while reducing computational burden
3Measurement precision
If more training examples are collected to improve modality recognition accuracy, then classification performance can be enhanced, but data storage requirements and processing time increase
Solution Approach 1:
The patent uses transfer learning from ImageNet, copying the pre-trained feature extraction capabilities to the medical imaging domain. This allows the system to achieve good classification performance without collecting and processing large volumes of annotated medical imaging data, as the model has already learned relevant features from the source domain (ImageNet) that can be transferred and adapted
Data Source
AI summary
Systems and methods for training a model for identifying an imaging modality. The systems and methods can be performed by a computer system having one or more processors and memory. A plurality of image vectors can be generated from first image data using a convolutional neural network. A loss function can be applied to each of the plurality of image vectors to produce an intermediate dataset. The intermediate dataset can be projected in a space having lower dimensional space that the intermediate dataset. A plurality of clusters can be identified from the intermediate dataset in the space using a clustering technique. Each of the plurality of clusters can be classified into one of a plurality of imaging modalities.