Joint Cell Region Classification in Digital Pathology
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Solution Overview
Problem
Current digital pathology systems face challenges in efficiently processing and analyzing large images of tissue slides, particularly in joint cell detection, segmentation, and classification, which are crucial for accurate diagnosis and research.
Innovation Solution
The proposed solution involves a deep learning approach using a multilayer neural network that simultaneously classifies cells and regions within digital pathology images. This method leverages semi-automated image analysis and ground truth data generated by pathologists to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional separate cell detection and region classification methods are used, then each task can be optimized independently, but the overall processing time and computational complexity increase significantly
Solution Approach 1:
The patent merges cell detection and region classification into a single unified deep learning model that processes images at multiple scales simultaneously. The model integrates cell-level feature extraction with region-level semantic understanding in one architectural framework, enabling joint optimization of both tasks and eliminating the sequential processing overhead of separate methods.
Solution Approach 2:
The patent introduces multi-scale processing by processing images at different resolution levels simultaneously. The deep learning model incorporates feature pyramids and multi-scale convolutional layers that analyze both local cell details and global region context in parallel, adding a dimensional aspect to the processing that enables simultaneous high-accuracy detection and classification.
2Measurement precision
If manual ground truth annotation by pathologists is performed, then classification accuracy is improved, but the time required for training data preparation increases
Solution Approach 1:
The patent employs preliminary automated detection and segmentation algorithms that prepare initial annotations before pathologist review. These pre-processing steps include automatic cell detection, nuclei segmentation, and preliminary classification that create a draft ground truth dataset, reducing the time pathologists need to spend on manual annotation while maintaining high accuracy through expert verification.
3Reliability
If deep learning models are trained on large datasets, then classification performance improves, but the computational resources and training time required increase
Solution Approach 1:
The patent segments the training process into efficient stages using pre-trained models and transfer learning. Instead of training from scratch on large datasets, the system utilizes pre-trained deep learning models that have learned general features, then fine-tunes them on specialized pathology data. This segmentation of the training process significantly reduces computational energy requirements while maintaining high accuracy.
Data Source
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AI summary
The present disclosure relates to automated systems and methods for training a multilayer neural network to jointly and simultaneously classify cells and regions from a set of training images. The present disclosure also relates to automated systems and methods for using a trained multilayer neural network to classify cells within an unlabeled image.