A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical Applications

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Citation: Edward Herskovits. (1990) A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical Applications. In KSL-90-46, 1990.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Edward Herskovits
title A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical Applications
number KSL-90-46
institution Knowledge Systems, AI Laboratory
year 1990
Bibtex more
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abstract We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance (MR) images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with his best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. This system consists of a preprocessing module; a semiautomatic, reliable procedure for obtaining objective estimates of an expert's opinion of an image's tissue composition; a classification module based on a combination of the maximum-likelihood (ML) classifier and the ISODATA unsupervised-clustering algorithm;and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. Furthermore, the combination of a clustering algorithm and a statistical classifier provides advantages not found in systems using either method alone.

KSL Technical Report ID: KSL-90-46
Facts about A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical ApplicationsRDF feed
Abstract We describe the design, implementation, an … We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance (MR) images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with his best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. This system consists of a preprocessing module; a semiautomatic, reliable procedure for obtaining objective estimates of an expert's opinion of an image's tissue composition; a classification module based on a combination of the maximum-likelihood (ML) classifier and the ISODATA unsupervised-clustering algorithm;and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. Furthermore, the combination of a clustering algorithm and a statistical classifier provides advantages not found in systems using either method alone. ound in systems using either method alone.
Author Edward Herskovits  +
Bibtype techreport  +
Has author Edward Herskovits  +
Has identifier KSL-90-46  +
Has publishing details 1990  +
Has title A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical Applications  +
Has where published KSL-90-46  +
Has year 1990  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-90-46  +
Number KSL-90-46  +
Process note YES  +
Title A Hybrid Classifier for Automated Radiologic Diagnosis: Preliminary Results and Clinical Applications  +
Year 1990  +
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