Neural Network Disease Diagnosis Using HSV Tissue Masking
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Solution Overview
Problem
Conventional disease diagnosis methods using neural networks are not robust to color variations caused by dyeing reagents and often misidentify non-tissue areas as diseased, lacking accuracy in visualizing the specific tissue parts affected by diseases like prostate cancer.
Innovation Solution
A disease diagnosis system that uses a neural network to consider surrounding patches in addition to specific patches, employing a heatmap generation module, tissue mask generation module, and visualization module to generate accurate diagnosis results, utilizing the HSV model for binarization and Conditional Random Fields for clear visualization of diseased tissue parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural network methods use only specific patch features for disease diagnosis, then the diagnostic process is simple and fast, but the diagnosis accuracy is insufficient because surrounding tissue context is ignored
Solution Approach 1:
The diagnostic process is segmented into two distinct neural network components: a patch-level neural network for analyzing individual tissue patches and a slide-level neural network for analyzing the entire tissue slide. This segmentation allows the system to capture both local disease features and global tissue context, thereby improving diagnosis accuracy while maintaining manageable computational complexity through modular architecture
Solution Approach 2:
The system merges the outputs of the patch-level neural network and slide-level neural network through a fusion mechanism. The patch-level features (capturing detailed local tissue characteristics) and slide-level features (capturing overall tissue architecture and context) are combined to produce the final disease diagnosis, enabling the system to leverage both fine-grained and coarse-grained information for more accurate diagnosis
2Reliability
If the neural network uses color values (RGB) as input data, then the processing is straightforward, but the network becomes sensitive to dyeing reagent variations and loses robustness
Solution Approach 1:
The system transforms the input color data from standard RGB color space to HSV (Hue, Saturation, Value) color space. This parameter transformation separates the chromatic information (hue) from the intensity information (value), making the feature extraction more robust to variations in dyeing reagents. The hue channel captures the fundamental color characteristics that are more stable across different staining conditions, while the value channel captures intensity information
3Reliability
If the system visualizes disease results at patch level, then the processing is simple, but non-tissue areas are incorrectly visualized as diseased, reducing diagnostic reliability
Solution Approach 1:
The system introduces an intermediary organization layer that acts as a mediator between the patch-level diagnosis results and the final visualization. This intermediary layer aggregates and filters the patch-level results, using the slide-level contextual information to distinguish between actual disease areas and non-tissue areas. The intermediary processing ensures that only clinically relevant disease regions are visualized, improving diagnostic reliability while maintaining clear and interpretable output
Data Source
AI summary
A system for disease diagnosis includes a patch neural network for generating a patch-level diagnostic result of whether or not a disease is present in each of predetermined patches formed by dividing a slide into a predetermined size; a heat map generation module for generating a patch-level heat map image corresponding to the biometric image obtained from the slide on the basis of the patch diagnostic results of the respective multiple patches included in the slide; a tissue mask generation module for generating a tissue mask image corresponding to the biometric image obtained from the slide on the basis of a hue-saturation-value (HSV) model corresponding to the slide; and a visualization module for generating a disease diagnostic visualization image corresponding to the biometric image obtained from the slide on the basis of the patch-level heat map image and the tissue mask image.