Automated Tumor Detection in Digitized Tissue Slides
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Solution Overview
Problem
Current machine learning systems are inadequate for effectively identifying and classifying tumors in tissue slides, and they fail to properly annotate or mark these slides to assist pathologists in diagnosis, resulting in significant time wastage and inefficiency.
Innovation Solution
A method for automatically detecting and classifying tumor regions in tissue slides using a combination of tissue classification modules and tumor classification models, which involve obtaining digitized slides, determining tissue types, identifying regions of interest (ROI) corresponding to tumors, generating classified slides with estimated diameters, and displaying these slides with user interface elements for pathologist input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by pathologists is used, then diagnostic accuracy is maintained, but time consumption increases significantly
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary tool that assists pathologists by pre-identifying and classifying tumor regions in tissue slides. This intermediary system processes images using machine learning algorithms to detect tumor presence, estimate diameters, and generate classified slides, thereby reducing the time pathologists need to spend on manual analysis while maintaining diagnostic accuracy through pathologist verification of automated results.
2Loss of time
If existing machine learning systems are used, then time consumption is reduced, but tumor identification accuracy deteriorates
Solution Approach 1:
The patent implements parameter changes by training machine learning models on specialized datasets of histopathology images with annotated tumor regions. The system adjusts classification parameters including tumor diameter thresholds, confidence score cutoffs, and classification criteria to optimize both speed and accuracy. The model learns from labeled data to accurately distinguish tumor from non-tumor regions while maintaining efficient processing speeds.
3Productivity
If automated classification is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The patent segments the diagnostic workflow into distinct automated and manual components. The automated system handles image preprocessing, tumor region detection, diameter estimation, and initial classification to generate structured output. The pathologist then focuses on verifying and finalizing diagnoses based on pre-processed information. This segmentation increases productivity by automating routine tasks while keeping system complexity manageable through modular design.
4Measurement precision
If multiple magnification levels are analyzed, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent applies preliminary action by first analyzing tissue slides at lower magnification levels to identify potential tumor regions efficiently. The automated system performs initial screening at reduced resolution to locate areas of interest, then selectively applies higher magnification analysis only to identified tumor regions. This preliminary sorting approach maintains measurement precision for final tumor characterization while significantly reducing overall time consumption by avoiding exhaustive high-magnification analysis of entire slides.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for automatically detecting and classifying tumor regions in a tissue slide. The method generally includes obtaining a digitized tissue slide from a tissue slide database and determining, based on output from a tissue classification module, a type of tissue of shown in the digitized tissue slide. The method further includes determining, based on output from a tumor classification model for the type of tissue, a region of interest (ROI) of the digitized tissue slide and generating a classified slide showing the ROI of the digitized tissue slide and an estimated diameter of the ROI. The method further includes displaying on an image display unit, the classified slide and user interface (UI) elements enabling a pathologist to enter input related to the classified slide.