Deep Learning Dermatopathology Diagnosis Automation
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Solution Overview
Problem
Subjective human assessment in dermatopathology leads to misdiagnosis due to the inherent subjectivity in evaluating tissue samples, which can impact patient care and diagnostic accuracy.
Innovation Solution
A computer-aided method and system using deep learning models, specifically convolutional neural networks and support vector machines, to classify human cutaneous tissue specimens by preprocessing whole-slide images, labeling pixels, and applying discriminative classifiers to obtain specimen-level diagnoses, such as basal cell carcinoma, dermal nevus, or seborrheic keratosis, with optional human confirmation and confidence threshold validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human observers conduct tissue sample assessments, then diagnostic flexibility and adaptability are maintained, but subjectivity leads to misdiagnosis and reduced reliability
Solution Approach 1:
The patent replaces the mechanical system of human visual assessment with an automated image processing system using convolutional neural networks and discriminative classifiers. The system processes whole-slide images through multiple computational stages: preprocessing, pixel-level classification, region identification, and specimen-level diagnosis, eliminating human subjectivity while maintaining diagnostic capability
Solution Approach 2:
The patent introduces an intermediary computational layer between the raw tissue image and the final diagnosis. This intermediary system uses deep learning models to extract features and generate diagnostic recommendations, acting as a mediator that enhances reliability without completely eliminating human oversight in the workflow
2Measurement precision
If automated deep learning models are applied to classify tissue specimens, then diagnostic accuracy and reliability improve, but system complexity increases
Solution Approach 1:
The patent segments the diagnostic process into distinct computational stages: image preprocessing, pixel-level classification using convolutional neural networks, contiguous region identification, and specimen-level diagnosis using discriminative classifiers. This segmentation allows each component to be optimized independently while maintaining overall system manageability
Solution Approach 2:
The patent implements a nested architecture where pixel-level classification results are nested within region-level analysis, which is in turn nested within specimen-level diagnosis. The convolutional neural network outputs are fed into the discriminative classifier, creating a hierarchical nested structure that manages complexity through layered processing
3Productivity
If whole-slide images are processed through deep learning models, then diagnostic speed and productivity increase, but computational resource requirements increase
Solution Approach 1:
The patent performs preliminary preprocessing of whole-slide images before applying the computationally intensive deep learning models. This includes image normalization, feature extraction, and preliminary filtering that reduces the complexity of subsequent processing steps, thereby reducing overall computational energy requirements while maintaining diagnostic productivity
Data Source
AI summary
Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.