Siamese Neural Network Encoder for Digital Pathology Tile Processing
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Solution Overview
Problem
Conventional digital pathology techniques face challenges in processing large Whole Slide Tissue Images (WSIs) due to their limited field of view and require time-consuming tile-level annotations, leading to suboptimal prediction performance and inefficiencies in handling arbitrarily sized input images.
Innovation Solution
A neural network (NN) with multiple encoder branches, sharing the same parameters, processes multiple tiles in parallel to produce a joined prediction, enabling efficient aggregation of latent feature data structures and inference of semantic histopathology features without auxiliary annotations, employing an encoder-decoder architecture for end-to-end training and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional digital pathology techniques process large Whole Slide Tissue Images, then the field of view is limited, but processing time and computational resources increase significantly
Solution Approach 1:
The large Whole Slide Tissue Image is divided into multiple smaller tiles that can be processed in parallel by multiple encoder branches of the neural network. This segmentation allows the system to handle arbitrarily sized images by processing them in manageable chunks simultaneously, expanding the effective field of view without proportionally increasing processing time
Solution Approach 2:
Multiple encoder branches process different tiles in parallel and their outputs are merged through a decoder branch to produce a unified prediction for the entire image. This merging approach combines the results from multiple parallel processing streams, achieving both expanded field of view and efficient processing time through parallel computation
2Measurement precision
If tile-level annotations are used for training, then the neural network can be trained on segmented regions, but annotation time and complexity increase
Solution Approach 1:
The neural network is designed to perform multiple functions: it can process both tile-level data and whole-slide images using the same architecture. The encoder-decoder structure with parameter sharing allows the model to learn from tile-level annotations while maintaining the capability to produce unified predictions for entire slides, reducing the need for separate annotation processes
3Productivity
If multiple encoder branches process multiple tiles in parallel, then processing efficiency improves, but model complexity increases
Solution Approach 1:
Multiple encoder branches are created as copies of a single encoder architecture, each processing different tiles in parallel. This copying approach allows efficient parallel processing while maintaining model simplicity through parameter sharing across all encoder branches, reducing the actual number of unique parameters that need to be trained compared to having completely independent encoders
4Adaptability or versatility
If conventional CNNs process large images, then they struggle with arbitrarily sized inputs, but the neural network architecture proposed handles any image size
Solution Approach 1:
The input image is segmented into multiple tiles that can be of any size, and the number of tiles adjusts dynamically based on the input image dimensions. This segmentation strategy enables the model to handle arbitrarily sized images by adapting the number and arrangement of tiles, while the shared encoder architecture maintains consistency across different input sizes
Solution Approach 2:
The architecture dynamically adapts to different input image sizes by adjusting the number of encoder branches and tiles processed. The system can scale the number of parallel processing units based on the input dimensions, providing dynamic adaptability to various image sizes without requiring completely different model architectures
Data Source
AI summary
Various example embodiments pertain to processing images that depict tissue samples using a neural network algorithm. The neural network algorithm includes multiple encoder branches that are copies of each other that share the same parameters. The encoder branches can, accordingly, be referred to as Siamese copies of each other.