Image Classification via Model Adaptation and Segmentation
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Solution Overview
Problem
Radiologists face significant challenges in classifying image data due to the time-consuming nature of describing anatomy, which requires analyzing numerous images and often involves manual input, leading to increased workloads.
Innovation Solution
A system that automatically classifies image data by adapting a model to the object within the data, utilizing a segmentation unit to segment the image and a classification unit to assign classes based on computed attributes, reducing the need for user input and enabling efficient image data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiologists manually describe anatomy by analyzing numerous images, then classification accuracy can be achieved, but workload and time consumption increase significantly
Solution Approach 1:
The system enables automatic classification by having the computer execute instructions to adapt models to image data, compute attributes, and assign classes without requiring radiologist input for each classification task, thus reducing manual workload while maintaining accuracy
Solution Approach 2:
The manual process of radiologist analysis is replaced by an automated computer-based system that uses model adaptation and attribute computation to perform classification, substituting human mechanical analysis with automated computational processes
2Measurement precision
If radiologists manually analyze and describe image data, then detailed classification can be achieved, but productivity decreases due to increased workload
Solution Approach 1:
The classification system operates autonomously by automatically adapting models to image data, computing relevant attributes, and assigning classes without requiring radiologist intervention, thereby improving productivity while preserving detailed classification capabilities
Solution Approach 2:
The system extracts only the essential information needed for classification by adapting models to key features in the image data and computing specific attributes, rather than requiring comprehensive manual analysis of all image details
3Measurement precision
If extensive user input is required for image classification, then classification accuracy can be maintained, but ease of operation deteriorates
Solution Approach 1:
The system performs classification automatically using model adaptation and attribute computation based on the image data itself, eliminating the need for extensive user input while maintaining classification accuracy through automated decision-making processes
Data Source
AI summary
The invention relates to a system (100) for classifying image data on the basis of a model for adapting to an object in the image data, the system comprising a segmentation unit (110) for segmenting the image data by adapting the model to the object in the image data and a classification unit (120) for assigning a class to the image data on the basis of the model adapted to the object in the image data, thereby classifying the image data, wherein the classification unit (120) comprises an attribute unit (122) for computing a value of an attribute of the model on the basis of the model adapted to the object in the image data, and wherein the assigned class is based on the computed value of the attribute. Thus, the system (100) of the invention is capable of classifying the image data without any user input. All inputs required for classifying the image data 10 constitute a model for adapting to an object in the image data. A person skilled in the art will understand however that in some embodiments of the system (100), a limited number of user inputs may be enabled to let the user influence and control the system and the classification process.