Reducing false positive responses in lung nodule detector system by asymmetric AdaBoost

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03144356" target="_blank" >RIV/68407700:21230/08:03144356 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Reducing false positive responses in lung nodule detector system by asymmetric AdaBoost

Original language description

We are developing a complex computer aided diagnosis (CAD) system to detect small pulmonary nodules from helical CT scans. Here we present a classifier to reduce the number of false positive responses of the primary detector. Our approach is based on anasymmetric Adaboost which enables us to give different weights to missed nodules (false negatives, FNs) and incorrectly detected structures (false positives, FPs). This is useful because there are noticeably more negative examples in the nodule candidateset than real nodules-true positives (TPs). The whole system is meant as a second opinion for a human radiologist to speed up reading the examination. That is why we should detect as many true nodules as possible, while a certain number of FPs is acceptable. The system was tested on 147 cases (36559 slices) containing 357 nodules marked by an expert radiologist. The new classifier significantly reduced the number of false positives, while only a few nodules were incorrectly omitted.

Czech name

Reducing false positive responses in lung nodule detector system by asymmetric AdaBoost

Czech description

We are developing a complex computer aided diagnosis (CAD) system to detect small pulmonary nodules from helical CT scans. Here we present a classifier to reduce the number of false positive responses of the primary detector. Our approach is based on anasymmetric Adaboost which enables us to give different weights to missed nodules (false negatives, FNs) and incorrectly detected structures (false positives, FPs). This is useful because there are noticeably more negative examples in the nodule candidateset than real nodules-true positives (TPs). The whole system is meant as a second opinion for a human radiologist to speed up reading the examination. That is why we should detect as many true nodules as possible, while a certain number of FPs is acceptable. The system was tested on 147 cases (36559 slices) containing 357 nodules marked by an expert radiologist. The new classifier significantly reduced the number of false positives, while only a few nodules were incorrectly omitted.

Type

D - Article in proceedings

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2008

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

ISBN

978-1-4244-2002-5

ISSN

1945-7928

e-ISSN

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Number of pages

4

Pages from-to

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Publisher name

IEEE

Place of publication

New York

Event location

Paris

Event date

May 14, 2008

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000258259800165