Histopathology Image Classification Using Multi-Level CNN-LSTM Architecture
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Solution Overview
Problem
Current machine learning diagnostics systems for histopathology images lack the ability to effectively utilize hierarchical relationships between characteristics at different magnification levels, limiting their diagnostic accuracy.
Innovation Solution
A deep learning architecture that includes convolutional neural networks (CNNs) and a long-short term memory (LSTM) network, which processes patches at multiple magnification levels to extract hierarchical relationships between tissue morphology and cellular structures, using an attention mechanism to identify regions of interest and generate patch-level classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep learning architecture with CNNs and LSTM network is used to extract hierarchical relationships at multiple magnification levels, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The histopathology image is divided into multiple patches at different magnification levels (e.g., 10x, 20x, 40x), and each patch is processed by a dedicated CNN. This segmentation allows the system to capture hierarchical relationships between tissue morphology at lower magnifications and cellular structures at higher magnifications, improving diagnostic accuracy while managing complexity through modular processing
Solution Approach 2:
The architecture implements a nested structure where CNNs at different magnification levels are processed sequentially by an LSTM network. The LSTM captures temporal dependencies between hierarchical levels, nesting the analysis from coarse (10x) to fine (40x) details. This nested approach enables the system to learn semantic relationships across scales without requiring a single overly complex model
2Measurement precision
If multiple patches at different magnification levels are processed to learn hierarchical relationships, then classification performance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by cropping and organizing patches at multiple magnification levels before main analysis. Attention mechanisms identify regions of interest in advance, allowing the LSTM to focus computational resources on diagnostically relevant areas rather than processing all patches uniformly, thus reducing overall processing time while maintaining classification performance
Solution Approach 2:
The attention mechanism selectively processes only the most relevant patches and magnification levels for each diagnostic task, rather than uniformly processing all available data. This partial action approach reduces processing time by eliminating redundant computations while maintaining or improving classification performance through focused analysis of critical features
Data Source
AI summary
Systems and methods for classification of histopathology images are disclosed. In one aspect, an apparatus for detecting a medical condition in a histopathology image includes a hardware memory configured to store executable instructions and a hardware processor in communication with the hardware memory, wherein the executable instructions, when executed by the processor, cause the processor to obtain a plurality of patches at a plurality of magnification levels from the histopathology image, apply a deep learning algorithm to each of the patches, extract, from applying the deep learning algorithm, information representative of a hierarchical relationship that links characteristics of the histopathology image present at one level and another level of the plurality of magnification levels, and identify the medical condition based on the extracted information representative of the hierarchical relationship for characteristics present at the one level and at the another level of the plurality of magnification levels.