Cross-Phase CT Attention for Renal Tumor Subtype Classification
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Solution Overview
Problem
Existing medical imaging techniques, particularly multi-phase CT scanning, struggle to accurately differentiate between renal cell carcinoma subtypes due to minute image feature differences and variations in enhancement patterns, leading to potential misdiagnosis and unnecessary surgeries.
Innovation Solution
An apparatus and method utilizing a lesion segmentation network, lesion-level feature embedding, cross-phase attention module, and feed forward network to analyze multi-phase CT images, capturing interdependence of lesion features across phases and predicting tumor subtypes with enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If multi-phase CT scanning is used for renal tumor diagnosis, then detection capability is improved, but measurement precision of tumor subtype differentiation deteriorates due to minute image feature differences
Solution Approach 1:
The patent segments the renal tumor analysis into multiple independent feature extraction components: shape features, texture features, and enhancement pattern features are extracted separately from different CT phases. This segmentation allows each feature type to be analyzed independently and then integrated, improving the overall measurement precision of tumor subtype differentiation while maintaining the comprehensive detection capability of multi-phase CT scanning.
2Productivity
If visual estimation by experienced radiologists is used, then diagnostic speed is improved, but measurement precision of tumor subtype classification deteriorates due to subjective variability
Solution Approach 1:
The patent implements a feedback mechanism where the system automatically extracts and analyzes multiple features from multi-phase CT images, then integrates these features to produce a classification result. This automated feedback loop eliminates subjective variability while maintaining diagnostic speed, as the system processes all relevant features systematically without human intervention, thereby improving measurement precision while preserving productivity.
3Device complexity
If traditional CT image analysis is used, then device complexity is minimized, but reliability of tumor subtype diagnosis deteriorates due to misdiagnosis of benign lesions
Solution Approach 1:
The patent changes multiple parameters simultaneously: it analyzes features across multiple CT phases (time parameter), extracts multiple types of features (shape, texture, enhancement patterns), and integrates these parameters using a systematic approach. This multi-parameter analysis significantly improves diagnosis reliability by distinguishing benign lesions from malignant tumors more accurately, while the modular implementation keeps device complexity manageable.
Data Source
AI summary
An apparatus for classifying subtypes of tumors includes: a lesion segmentation network module for extracting lesion segmentation maps from multi-phase CT images; a lesion-level feature embedding module for acquiring lesion-level feature embeddings using the multi-phase CT images and the lesion segmentation maps; a cross-phase attention module for acquiring an attention weight matrix representing interdependence of multi-phase pairwise lesion features using the feature embeddings and combining the feature embeddings and the attention weight matrix to produce an output feature matrix; and a feed forward network module for predicting a probability for the classification of the subtypes of tumors through the input of the output feature matrix.