Telemedicine Image Compression Neural Network
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Solution Overview
Problem
Telemedicine data transfer systems face challenges in efficiently transferring ophthalmic data, such as imagery, due to high data volume and latency issues, requiring efficient image quality assessment and compression to minimize bandwidth and storage requirements while maintaining diagnostic quality.
Innovation Solution
A telemedicine data transfer system incorporating an image quality assessment circuit using deep neural networks to evaluate and optimize image transfer, and an image compression/decompression framework utilizing neural networks to customize compression strategies based on image type and application, prioritizing important image regions and applying enhancements for reduced bandwidth and enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image compression is applied to reduce data volume, then bandwidth requirements and storage needs are reduced, but image quality may deteriorate and diagnostic accuracy may be compromised
Solution Approach 1:
The patent applies different compression strategies to different regions of the image based on their diagnostic importance. Critical regions such as lesions, tumors, or areas with pathological features are preserved with high quality or minimal compression, while non-critical background areas undergo more aggressive compression. This regional differentiation allows significant overall data reduction while maintaining diagnostic accuracy in important areas.
Solution Approach 2:
The system dynamically adjusts compression parameters based on image content analysis, diagnostic requirements, and transmission conditions. Compression ratios, quality levels, and algorithm selections are adapted according to the specific characteristics of each image region and the clinical context, enabling optimized balance between data reduction and quality preservation for different types of medical imagery.
2Manufacturing precision
If high-resolution medical images are transmitted without compression, then diagnostic quality is maintained, but bandwidth consumption increases and transfer time is extended
Solution Approach 1:
The system extracts and prioritizes transmission of critical diagnostic information from full-resolution images. Through content-aware analysis, the system identifies and extracts essential diagnostic features, regions of interest, and clinically relevant data, transmitting only these extracted elements at high quality while reducing or omitting transmission of redundant non-diagnostic areas, thereby maintaining diagnostic capability while reducing transfer time.
Solution Approach 2:
Medical images are segmented into multiple regions based on diagnostic importance, with critical areas identified and separated from non-critical background regions. The segmentation enables differential transmission strategies where high-priority segments are transmitted with minimal compression and higher bandwidth allocation, while low-priority segments are compressed more aggressively or transmitted with lower priority, reducing overall transfer time while preserving essential diagnostic information.
3Measurement precision
If multiple deep neural networks are trained for different modalities and requirements, then image assessment accuracy is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent develops a universal deep neural network framework that can handle multiple imaging modalities (such as fundus images, OCT scans, and other ophthalmic imaging types) and different assessment requirements through a single integrated system. The network is designed with modular architecture and adaptive mechanisms that allow it to process diverse image types and apply appropriate assessment criteria, reducing the need for completely separate networks for each modality while maintaining high assessment accuracy across different image types.
Data Source
AI summary
A telemedicine data transfer system receives telemedicine data from a data source and decides whether to transmit the telemedicine data to a remote end circuit or prevent transmission of the telemedicine data to the remote end circuit based on a result of an evaluation. A telemedicine data transfer system includes image compression and decompression circuits. The decompression circuit may produce decompressed and enhanced telemedicine data. The image compression and decompression circuits include neural networks trained using an objective function to evaluate a difference between a training input data provided to the image compression circuit and a training output data that is output from the image decompression circuit. The training output data is made different from the training input data by a data enhancement operation performed on the training output data prior to the evaluation.