CNN Histopathology Classification for Cancer Mutation Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional analysis of histopathological images for cancer diagnosis is time-consuming and prone to intra- and inter-observer variability, and existing deep learning models are limited to specific cancer types, lacking versatility and computational efficiency.
Innovation Solution
A container-based orchestration system is used to deploy a deep learning model trained on a diverse set of histological and non-histological images, enabling efficient classification of cancerous and non-cancerous tissues across various cancer types, and identifying genetic mutations, using convolutional neural networks (CNNs) with transfer learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of histopathological images by trained pathologists is used, then diagnostic accuracy can be maintained, but the analysis process becomes time-consuming and prone to intra- and inter-observer variability
Solution Approach 1:
The patent replaces the mechanical system of manual pathologist inspection with an automated deep learning model that processes histopathological images. The convolutional neural network (CNN) architecture automatically extracts features and classifies cancer types, substituting human visual analysis with computational image processing while maintaining diagnostic accuracy and eliminating intra- and inter-observer variability.
Solution Approach 2:
The deep learning model performs self-service by automatically analyzing histopathological images without requiring manual intervention. The system independently processes images, extracts relevant features, and generates diagnostic classifications, enabling autonomous operation that reduces dependency on manual pathologist review while improving efficiency.
2Extent of automation
If existing deep learning models are used for cancer classification, then automation is achieved, but the models are limited to specific cancer types and lack versatility
Solution Approach 1:
The patent implements a universal deep learning model architecture that can classify multiple cancer types including breast, lung, and colorectal cancers within a single system. The model is designed to handle diverse histopathological images across different cancer types, making it multi-functional rather than specialized for a single cancer type, thereby achieving both automation and versatility.
Solution Approach 2:
The model achieves versatility through parameter adjustments and fine-tuning that allow it to adapt to different cancer types. By modifying model parameters and training data configurations, the same underlying architecture can be optimized for various cancer classifications, enabling the system to handle multiple cancer types without requiring completely separate models.
3Adaptability or versatility
If deep learning models are trained on diverse datasets including non-histological images, then model versatility improves, but computational intensity and training time increase
Solution Approach 1:
The patent applies transfer learning by pre-training the deep learning model on large-scale non-histological image datasets (such as ImageNet) before fine-tuning on histopathological images. This preliminary training establishes a robust feature extraction capability that can be transferred to medical imaging, reducing the computational burden and data requirements for subsequent specialized training while maintaining model versatility.
Solution Approach 2:
The training process is segmented into distinct phases: initial pre-training on general images, followed by fine-tuning on histopathological data, and finally specialized training for specific cancer types. This segmentation allows the model to build general image understanding first, then progressively specialize, reducing overall computational intensity compared to training from scratch on all data simultaneously.
Data Source
AI summary
Techniques for classifying, using a deep learning model, histopathological whole slide images (WSIs) as comprising images of cancerous or non-cancerous tissue and/or as comprising images of cancerous tissue having a genetic mutation or not having a genetic mutation are described herein. The techniques include at least one processor configured to instantiate a container-based processing architecture to train and/or use the deep learning model to process and classify at least one WSI. In some embodiments, a treatment may be selected and administered based on a classification result obtained from the deep learning model.