Pathology Slide Digitization via ML Region Identification
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Solution Overview
Problem
Current slide digitization methods lack efficiency in identifying regions of interest during the digitization process, which hampers accurate analysis and diagnosis of pathological conditions in biomedical specimens.
Innovation Solution
An apparatus and method utilizing an image capture device with a processor and memory to generate an initial scan of pathology slides, receive user datasets, identify regions of interest through a trained machine-learning model, and adjust device parameters for enhanced scanning, resulting in improved digitization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional slide digitization methods are used, then the scanning process covers the entire slide, but the efficiency in identifying regions of interest is low and diagnostic accuracy is hampered
Solution Approach 1:
The patent segments the slide scanning process into two distinct phases: a low-resolution preliminary scan to identify regions of interest, and a high-resolution focused scan of only those identified regions. This segmentation allows the system to balance efficiency (by scanning only relevant areas at high resolution) with diagnostic accuracy (by maintaining comprehensive low-resolution coverage), directly resolving the technical contradiction between scanning efficiency and diagnostic precision.
2Measurement precision
If the entire slide is scanned at high resolution, then diagnostic accuracy is improved, but the time and resources required for scanning increase
Solution Approach 1:
The patent applies partial action by performing a preliminary low-resolution scan of the entire slide to identify regions of interest, followed by focused high-resolution scanning only of those specific regions. This approach avoids the excessive time consumption of scanning the entire slide at high resolution while still achieving high digitization quality where it matters most for diagnosis, thereby resolving the contradiction between digitization quality and scanning time.
3Productivity
If machine learning models are trained to identify regions of interest, then scanning efficiency is improved, but the complexity of the system increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that act as intelligent mediators between the raw slide image data and the region identification process. These models are trained to automatically detect and flag regions of interest, thereby improving scanning efficiency without requiring complex manual intervention systems. The intermediary ML layer simplifies the overall system architecture compared to alternative approaches that would require extensive human annotation and validation.
Data Source
AI summary
An apparatus for identifying regions of interest during slide digitization is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory contains instructions configuring the processor to receive a user dataset associated with at least a pathology slide. The memory contains instructions configuring the processor to identify one or more regions of interest within at least a pathology slide as a function of the user dataset. The memory contains instructions configuring the processor to identify at least one scan parameter as a function of the one or more regions of interest. The memory contains instructions configuring the processor to generate a digitized slide by scanning the at least a pathology slide as a function the at least one scan parameter.