Multi-step Image Recognition Framework for Digital Pathology
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Solution Overview
Problem
Conventional pathology image analysis methods require significant human labor, are time-consuming, and suffer from high memory and processing delays due to the need to handle high-dimensional data sets, limiting the effectiveness of single-step image recognition frameworks in digitized pathology images.
Innovation Solution
A multi-step image recognition framework that gradually builds models by reducing the number of ground truth dimensions and features in each step, utilizing a multi-layer feature extraction method to classify digital pathology images, allowing for the differentiation of image regions and reducing computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single-step image recognition framework is used, then classification can be completed in one step, but memory requirements and processing time increase significantly due to high-dimensional data
Solution Approach 1:
The patent applies segmentation by dividing the image recognition task into multiple sequential steps, where each step processes a subset of features and ground truth dimensions. This breaks down the high-dimensional data processing into manageable chunks, reducing peak memory requirements while maintaining overall classification accuracy.
Solution Approach 2:
The patent transforms the problem from a single-step high-dimensional processing task into a multi-step sequence of lower-dimensional tasks. By adding the temporal dimension of multiple processing steps, the system handles data in a sequential manner that reduces memory footprint at any given moment.
2Measurement precision
If all features and ground truth dimensions are kept in memory simultaneously, then complete classification can be achieved, but processing delays increase
Solution Approach 1:
The classification process is segmented into multiple steps, each handling specific features and ground truth dimensions. This allows the system to maintain classification accuracy by systematically processing different aspects of the data in sequence, rather than requiring all data to be loaded simultaneously into memory.
Solution Approach 2:
The patent performs preliminary processing in earlier steps by extracting and processing certain features before moving to subsequent steps. This preliminary action allows later steps to work with already-processed data, reducing the need to hold all raw data in memory simultaneously.
3Reliability
If conventional training techniques are used with high-dimensional data, then model training can be performed, but only small subsets of training data can be used due to processor requirements
Solution Approach 1:
The training process is divided into multiple steps, where each step trains on a specific subset of features and ground truth dimensions. This segmentation enables the system to process larger overall training data volumes by distributing the training load across multiple passes, each handling manageable data subsets.
Solution Approach 2:
The patent performs partial training actions in each step, focusing on specific feature subsets rather than attempting to train on all features simultaneously. This allows the system to iterate through training data multiple times with different feature subsets, effectively utilizing larger training datasets than would be possible in a single-step approach.
Data Source
AI summary
Systems and methods for implementing a multi-step image recognition framework for classifying digital images are provided. The provided multi-step image recognition framework utilizes a gradual approach to model training and image classification tasks requiring multi-dimensional ground truths. A first step of the multi-step image recognition framework differentiates a first image region from a remainder image region. Each subsequent step operates on a remainder image region from the previous step. The provided multi-step image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-step image recognition frameworks.