TY - GEN
T1 - Veritas
T2 - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
AU - Cholleti, Sharath R.
AU - Goldman, Sally A.
AU - Blum, Avrim
AU - Politte, David G.
AU - Don, Steven
PY - 2008
Y1 - 2008
N2 - We consider a variation of the problem of combining expert opinions for the situation in which there is no ground truth to use for training. Even though we don't have labeled data, the goal of this work is quite different from an unsupervised learning problem in which the goal is to cluster the data into different groups. Our work is motivated by the application of segmenting a lung nodule in a computed tomography (CT) scan of the human chest. The lack of a gold standard of truth is a critical problem in medical imaging. A variety of experts, both human and computer algorithms, are available that can mark which voxels are part of a nodule. The question is, how to combine these expert opinions to estimate the unknown ground truth. We present the Veritas algorithm that predicts the underlying label using the knowledge in the expert opinions even without the benefit of any labeled data for training. We evaluate Veritas using artificial data and real CT images to which a synthetic nodule has been added, providing a known ground truth.
AB - We consider a variation of the problem of combining expert opinions for the situation in which there is no ground truth to use for training. Even though we don't have labeled data, the goal of this work is quite different from an unsupervised learning problem in which the goal is to cluster the data into different groups. Our work is motivated by the application of segmenting a lung nodule in a computed tomography (CT) scan of the human chest. The lack of a gold standard of truth is a critical problem in medical imaging. A variety of experts, both human and computer algorithms, are available that can mark which voxels are part of a nodule. The question is, how to combine these expert opinions to estimate the unknown ground truth. We present the Veritas algorithm that predicts the underlying label using the knowledge in the expert opinions even without the benefit of any labeled data for training. We evaluate Veritas using artificial data and real CT images to which a synthetic nodule has been added, providing a known ground truth.
UR - http://www.scopus.com/inward/record.url?scp=57649173427&partnerID=8YFLogxK
U2 - 10.1109/ICTAI.2008.141
DO - 10.1109/ICTAI.2008.141
M3 - Conference contribution
AN - SCOPUS:57649173427
SN - 9780769534404
T3 - Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
SP - 45
EP - 52
BT - Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Y2 - 3 November 2008 through 5 November 2008
ER -