Convolutional Neural Network Mask Annotation for Medical Image Classification
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Solution Overview
Problem
Conventional artificial intelligence techniques for digital image classification and analysis, such as region-of-interest based deep neural networks, face challenges in achieving accuracy and efficiency due to labor-intensive processes like pixel annotations, and often rely on image-level labels which limit their effectiveness.
Innovation Solution
A novel end-to-end deep learning framework is introduced, utilizing a convolutional neural network with a decoder and loss function component to improve classification and localization accuracy by employing mask annotations for regions of interest, enabling the detection and localization of features in medical images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional artificial intelligence techniques use image-level labels for training, then the training process is simpler, but the classification and localization accuracy is limited
Solution Approach 1:
The patent segments the annotation process from pixel-level manual labeling to object-level mask annotation. Instead of requiring detailed pixel-level boundaries, the system uses coarse mask annotations that define object regions, significantly reducing annotation complexity while maintaining high classification and localization accuracy through the decoder network that refines these masks.
Solution Approach 2:
The patent transitions from 2D image-level labels to 3D volumetric mask annotations in medical imaging. By incorporating depth information and spatial relationships through 3D masks, the system achieves superior localization accuracy compared to conventional 2D image-level classification, while the automated mask generation reduces the perceived complexity.
2Measurement precision
If pixel annotations are used for training deep neural networks, then localization accuracy improves, but the labor-intensive process reduces productivity
Solution Approach 1:
The patent implements self-service through automated mask generation from imaging data and iterative back-propagation that automatically adjusts parameters. The system generates its own training masks from the medical images without requiring manual pixel-level annotation, and the loss function automatically guides the optimization process, eliminating labor-intensive manual labeling while maintaining high localization accuracy.
Solution Approach 2:
The patent performs preliminary action by pre-processing images to generate initial mask annotations automatically before training begins. The system prepares training data with automated mask generation and data augmentation in advance, reducing the need for manual intervention during the training process and improving overall productivity.
3Reliability
If conventional techniques rely on image-level labels, then data preparation is easier, but the effectiveness in medical imaging analysis is reduced
Solution Approach 1:
The patent achieves universality by designing a multi-functional system that simultaneously performs classification, localization, and mask generation using the same convolutional neural network with decoder. The single framework handles multiple tasks (image classification, object localization, and mask refinement) that would traditionally require separate processing steps, improving analysis effectiveness while maintaining ease of data preparation through unified training.
Data Source
AI summary
Systems and techniques for classification and localization based on annotation information are presented. In one example, a system trains a convolutional neural network based on training data and a plurality of images. The training data is associated with a plurality of patients from at least one imaging device. The plurality of images is associated with a plurality of masks from a plurality of objects. The convolutional neural network comprises a decoder consisting of at least one up-sampling layer and at least one convolutional layer. The system also generates a loss function based on the plurality of masks, where the loss function is iteratively back propagated to tune parameters of the convolutional neural network. The system also predicts a classification label for an input image based on the convolutional neural network.