| 1. Title: Wisconsin Diagnostic Breast Cancer (WDBC) | |
| 2. Source Information | |
| a) Creators: | |
| Dr. William H. Wolberg, General Surgery Dept., University of | |
| Wisconsin, Clinical Sciences Center, Madison, WI 53792 | |
| [email protected] | |
| W. Nick Street, Computer Sciences Dept., University of | |
| Wisconsin, 1210 West Dayton St., Madison, WI 53706 | |
| [email protected] 608-262-6619 | |
| Olvi L. Mangasarian, Computer Sciences Dept., University of | |
| Wisconsin, 1210 West Dayton St., Madison, WI 53706 | |
| [email protected] | |
| b) Donor: Nick Street | |
| c) Date: November 1995 | |
| 3. Past Usage: | |
| first usage: | |
| W.N. Street, W.H. Wolberg and O.L. Mangasarian | |
| Nuclear feature extraction for breast tumor diagnosis. | |
| IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science | |
| and Technology, volume 1905, pages 861-870, San Jose, CA, 1993. | |
| OR literature: | |
| O.L. Mangasarian, W.N. Street and W.H. Wolberg. | |
| Breast cancer diagnosis and prognosis via linear programming. | |
| Operations Research, 43(4), pages 570-577, July-August 1995. | |
| Medical literature: | |
| W.H. Wolberg, W.N. Street, and O.L. Mangasarian. | |
| Machine learning techniques to diagnose breast cancer from | |
| fine-needle aspirates. | |
| Cancer Letters 77 (1994) 163-171. | |
| W.H. Wolberg, W.N. Street, and O.L. Mangasarian. | |
| Image analysis and machine learning applied to breast cancer | |
| diagnosis and prognosis. | |
| Analytical and Quantitative Cytology and Histology, Vol. 17 | |
| No. 2, pages 77-87, April 1995. | |
| W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. | |
| Computerized breast cancer diagnosis and prognosis from fine | |
| needle aspirates. | |
| Archives of Surgery 1995;130:511-516. | |
| W.H. Wolberg, W.N. Street, D.M. Heisey, and O.L. Mangasarian. | |
| Computer-derived nuclear features distinguish malignant from | |
| benign breast cytology. | |
| Human Pathology, 26:792--796, 1995. | |
| See also: | |
| http://www.cs.wisc.edu/~olvi/uwmp/mpml.html | |
| http://www.cs.wisc.edu/~olvi/uwmp/cancer.html | |
| Results: | |
| - predicting field 2, diagnosis: B = benign, M = malignant | |
| - sets are linearly separable using all 30 input features | |
| - best predictive accuracy obtained using one separating plane | |
| in the 3-D space of Worst Area, Worst Smoothness and | |
| Mean Texture. Estimated accuracy 97.5% using repeated | |
| 10-fold crossvalidations. Classifier has correctly | |
| diagnosed 176 consecutive new patients as of November | |
| 1995. | |
| 4. Relevant information | |
| Features are computed from a digitized image of a fine needle | |
| aspirate (FNA) of a breast mass. They describe | |
| characteristics of the cell nuclei present in the image. | |
| A few of the images can be found at | |
| http://www.cs.wisc.edu/~street/images/ | |
| Separating plane described above was obtained using | |
| Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree | |
| Construction Via Linear Programming." Proceedings of the 4th | |
| Midwest Artificial Intelligence and Cognitive Science Society, | |
| pp. 97-101, 1992], a classification method which uses linear | |
| programming to construct a decision tree. Relevant features | |
| were selected using an exhaustive search in the space of 1-4 | |
| features and 1-3 separating planes. | |
| The actual linear program used to obtain the separating plane | |
| in the 3-dimensional space is that described in: | |
| [K. P. Bennett and O. L. Mangasarian: "Robust Linear | |
| Programming Discrimination of Two Linearly Inseparable Sets", | |
| Optimization Methods and Software 1, 1992, 23-34]. | |
| This database is also available through the UW CS ftp server: | |
| ftp ftp.cs.wisc.edu | |
| cd math-prog/cpo-dataset/machine-learn/WDBC/ | |
| 5. Number of instances: 569 | |
| 6. Number of attributes: 32 (ID, diagnosis, 30 real-valued input features) | |
| 7. Attribute information | |
| 1) ID number | |
| 2) Diagnosis (M = malignant, B = benign) | |
| 3-32) | |
| Ten real-valued features are computed for each cell nucleus: | |
| a) radius (mean of distances from center to points on the perimeter) | |
| b) texture (standard deviation of gray-scale values) | |
| c) perimeter | |
| d) area | |
| e) smoothness (local variation in radius lengths) | |
| f) compactness (perimeter^2 / area - 1.0) | |
| g) concavity (severity of concave portions of the contour) | |
| h) concave points (number of concave portions of the contour) | |
| i) symmetry | |
| j) fractal dimension ("coastline approximation" - 1) | |
| Several of the papers listed above contain detailed descriptions of | |
| how these features are computed. | |
| The mean, standard error, and "worst" or largest (mean of the three | |
| largest values) of these features were computed for each image, | |
| resulting in 30 features. For instance, field 3 is Mean Radius, field | |
| 13 is Radius SE, field 23 is Worst Radius. | |
| All feature values are recoded with four significant digits. | |
| 8. Missing attribute values: none | |
| 9. Class distribution: 357 benign, 212 malignant |