Machine Learning Image Quality Classification for Teledermatology
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Solution Overview
Problem
Existing machine learning models for classifying dermatological images face challenges in determining image quality, leading to wasted clinician time and bandwidth due to the lack of standardization in image quality assessment.
Innovation Solution
A method is developed to train a machine learning model that classifies medical images by receiving a dataset of images, dividing them into segments, associating each segment with a label based on the image's quality, and training the model using these labels to differentiate between good and bad quality images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained to perform full diagnosis, then diagnostic capability is improved, but system complexity increases without clinician confidence
Solution Approach 1:
The patent divides the image classification task into two separate models: a quality classification model that assesses image quality, and a diagnostic model that performs medical diagnosis. This segmentation allows each model to specialize in one function, improving reliability of the quality assessment while keeping the diagnostic function separate and controlled by clinicians.
Solution Approach 2:
The quality classification model serves as an intermediary between image acquisition and clinical diagnosis. It filters images based on quality metrics before they reach clinicians, acting as a mediator that prepares data for subsequent diagnostic processes without replacing clinician judgment.
2Reliability
If uncompressed images are transmitted to clinicians for review, then image quality is preserved, but bandwidth and resources are wasted
Solution Approach 1:
The system performs preliminary quality assessment using the trained machine learning model before transmitting images to clinicians. This preliminary action filters out low-quality images that would waste bandwidth, while preserving high-quality images for transmission in their full resolution.
Solution Approach 2:
The system changes the transmission parameter (image compression level) based on the quality classification. High-quality images are transmitted uncompressed or with minimal compression, while low-quality images are either compressed more aggressively or filtered out entirely, optimizing bandwidth usage.
3Measurement precision
If clinicians review bad quality images, then diagnostic accuracy decreases, but clinician time is wasted
Solution Approach 1:
The quality classification model provides feedback about image quality before clinician review. This feedback mechanism allows the system to automatically identify and flag low-quality images, preventing clinicians from spending time on images that would not yield accurate diagnostic results.
Solution Approach 2:
The system performs self-service quality filtering using automated machine learning models, eliminating the need for clinicians to manually assess image quality. This self-service approach saves clinician time while maintaining diagnostic accuracy by ensuring only quality images reach the review stage.
Data Source
AI summary
Systems and methods using machine learning for classifying images as being sufficient for medical diagnosis. An example of the method includes: receiving a dataset comprising a plurality of medical images; receiving, from a first single source, a respective label for each one of the plurality of medical images, the respective label being a positive response versus a negative response; dividing each one of the plurality of medical images into a plurality of medical image segments; associating each one of the plurality of medical image segments with an image segment label based on the respective label for the respective medical image being divided; and training a machine learning model using: the plurality of medical images, the respective label for each one of the plurality of medical images, the plurality of medical image segments, and the respective image segment label of each one of the plurality of medical image segments.