Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study

Ben Van Calster; Dirk Timmerman; Ian T. Nabney; Lil Valentin; Caroline Van Holsbeke; Sabine Van Huffel

doi:10.1109/IEMBS.2006.260118

Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study

Ben Van Calster, Dirk Timmerman, Ian T. Nabney, Lil Valentin, Caroline Van Holsbeke, Sabine Van Huffel

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

14 Citations (Scopus)

Abstract

Ovarian masses are common and a good pre-surgical assessment of their nature is important for adequate treatment. Bayesian Multi-Layer Perceptrons (MLPs) using the evidence procedure were used to predict whether tumors are malignant or not. Automatic Relevance Determination (ARD) is used to select the most relevant of the 40+ available variables. Cross-validation is used to select an optimal combination of input set and number of hidden neurons. The data set consists of 1066 tumors collected at nine centers across Europe. Results indicate good performance of the models with AUC values of 0.93-0.94 on independent data. A comparison with a Bayesian perceptron model shows that the present problem is to a large extent linearly separable. The analyses further show that the number of hidden neurons specified in the ARD analyses for input selection may influence model performance.

Original language	English
Title of host publication	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	5342-5345
Number of pages	4
ISBN (Print)	1424400325
DOIs	https://doi.org/10.1109/IEMBS.2006.260118
Publication status	Published - 1 Dec 2006

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Van Calster, B., Timmerman, D., Nabney, I. T., Valentin, L., Van Holsbeke, C., & Van Huffel, S. (2006). Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (pp. 5342-5345). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IEMBS.2006.260118

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abstract = "Ovarian masses are common and a good pre-surgical assessment of their nature is important for adequate treatment. Bayesian Multi-Layer Perceptrons (MLPs) using the evidence procedure were used to predict whether tumors are malignant or not. Automatic Relevance Determination (ARD) is used to select the most relevant of the 40+ available variables. Cross-validation is used to select an optimal combination of input set and number of hidden neurons. The data set consists of 1066 tumors collected at nine centers across Europe. Results indicate good performance of the models with AUC values of 0.93-0.94 on independent data. A comparison with a Bayesian perceptron model shows that the present problem is to a large extent linearly separable. The analyses further show that the number of hidden neurons specified in the ARD analyses for input selection may influence model performance.",

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Van Calster, B, Timmerman, D, Nabney, IT, Valentin, L, Van Holsbeke, C & Van Huffel, S 2006, Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study. in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), pp. 5342-5345. https://doi.org/10.1109/IEMBS.2006.260118

Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study. / Van Calster, Ben; Timmerman, Dirk; Nabney, Ian T. et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2006. p. 5342-5345.

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

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T1 - Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study

AU - Van Calster, Ben

AU - Timmerman, Dirk

AU - Nabney, Ian T.

AU - Valentin, Lil

AU - Van Holsbeke, Caroline

AU - Van Huffel, Sabine

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AB - Ovarian masses are common and a good pre-surgical assessment of their nature is important for adequate treatment. Bayesian Multi-Layer Perceptrons (MLPs) using the evidence procedure were used to predict whether tumors are malignant or not. Automatic Relevance Determination (ARD) is used to select the most relevant of the 40+ available variables. Cross-validation is used to select an optimal combination of input set and number of hidden neurons. The data set consists of 1066 tumors collected at nine centers across Europe. Results indicate good performance of the models with AUC values of 0.93-0.94 on independent data. A comparison with a Bayesian perceptron model shows that the present problem is to a large extent linearly separable. The analyses further show that the number of hidden neurons specified in the ARD analyses for input selection may influence model performance.

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M3 - Chapter in a book

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BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

PB - Institute of Electrical and Electronics Engineers (IEEE)

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Van Calster B, Timmerman D, Nabney IT, Valentin L, Van Holsbeke C, Van Huffel S. Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Institute of Electrical and Electronics Engineers (IEEE). 2006. p. 5342-5345 doi: 10.1109/IEMBS.2006.260118

Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study

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