3D CNN Ensemble for Longitudinal Lung Cancer Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cancer detection methods, particularly in lung CT scans, suffer from limitations in sensitivity and specificity, and there is a need for improved automated systems that can accurately predict the presence and location of cancer using deep learning techniques.
Innovation Solution
A method and system utilizing an ensemble of deep learning models, including a global predictive model and a two-stage prediction model, to generate probabilistic predictions of cancer presence and location in CT datasets, using 3D convolutional neural networks with attention mechanisms and data augmentation to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual or semi-automated interpretation by radiologists is used, then flexibility and adaptability to complex cases is maintained, but consistency and standardization are reduced
Solution Approach 1:
The patent replaces manual radiological interpretation with an automated deep learning system that uses convolutional neural networks to detect and classify lung nodules. The system automatically processes CT images through multiple network layers, extracting features and making classifications without human intervention, thereby achieving both consistency and standardization while maintaining diagnostic capability
2Reliability
If manually designed guidelines like Lung-RADS and Fleischner guidelines are used, then standardization and consistency are improved, but sensitivity and specificity are limited by the criteria definitions
Solution Approach 1:
The patent transforms the fixed criteria-based approach into a data-driven parameter learning system. The deep learning model automatically learns optimal feature representations and classification thresholds from training data, enabling the system to adapt to subtle variations in imaging patterns that go beyond predefined guidelines, thereby improving sensitivity and specificity while maintaining standardization through consistent algorithmic application
Solution Approach 2:
The system combines multiple types of information processing within a unified deep learning architecture. It integrates spatial pattern recognition, texture analysis, and contextual feature extraction across multiple network layers, creating a composite diagnostic capability that surpasses single-criterion guidelines while maintaining standardized execution through the unified model
3Measurement precision
If deep learning models are used for cancer detection, then sensitivity and specificity can be improved, but device complexity increases
Solution Approach 1:
The patent divides the cancer detection task into distinct functional modules within the deep learning system. It separates nodule detection, nodule characterization, and malignancy classification into different processing stages handled by specialized network components. This segmentation allows each module to be optimized independently and simplifies the overall system architecture while maintaining high detection accuracy
Solution Approach 2:
The system introduces intermediate feature representation layers that serve as mediators between raw image data and final diagnostic predictions. These intermediate layers extract and transform imaging features into meaningful representations, simplifying the connection between input images and output diagnoses. The intermediary feature space acts as a bridge that reduces the complexity of direct mapping while preserving diagnostic information
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method and system to generate a probabilistic prediction of the presence/absence of cancer in longitudinal and current image datasets, and/or multimodal image datasets, and the location of the cancer, is described. The method and system uses an ensemble of deep learning models. The ensemble includes a global model in the form of a 3D convolutional neural network (CNN) extracting features in the datasets indicative of the presence of cancer on a global basis. The ensemble also includes a two-stage prediction model which includes a first stage or detection model which identifies cancer detection candidates (different cropped volumes of 3D data in the a dataset containing candidates which may be cancer) and a second stage or probability model which incorporates the longitudinal datasets (or multimodal images in a multimodal dataset) and the extracted features from the global model and assigns a cancer probability p to each of the cancer detection candidates. An overall prediction of probability of cancer is obtained from the probabilities assigned by the second stage model, e.g., using a Noisy-OR approach.