Please submit a ZIP archive containing the following:
- The experimental results as an excel file (.xls or .xlsx)* where the first ten columns of each row contain the numbers that are used for measuring the performance, as described on the evaluation page. Each row contains the numberical results of one experiment, where the crucial system parameters are slightly deviated from the optimum. The employed parameters for generating the given results are appended to the row, where the first row contains the headers with the parameter names**.
- A folder containing all annotated images, as annotated by the system with optimal parameter settings. The optimal system can be derived from the numerical results and participants can make a trade-off regarding what they consider the best performing system. Please add the JIGS and SSC score to the filename, where the decimal point is omitted., e.g. pat07_im1_ACHD_JIGS070_SCC081.png.
Please name your file as follows: RESULTS_<NAME>_<METHOD>.zip, where <NAME> is the family name of the participant and <METHOD> is a name for the proposed method.
Please upload your submission here.
(We have encountered some technical problems with the uploading system, if you are not able to use the upload button, please send your results to email@example.com)
* When the participant is not able to produce these file types, please contact Fons van der Sommen.
** For example: using a the Gabor-based approach as described in , some crucial system parameters are number of orientations (Q),the central frequency of the lowest band (W) and the fraction of positive training samples (PF). An excel file with results could look as follows:
|Pat. Sens.||Pat. Spec.||Pat. F1||Img. Sens.||Img. Spec.||Img. F1||JIGGS avg.||JIGGS std.||SSC avg.||SSC std.||Q||W||PF|
 F. van der Sommen, S. Zinger, E.J. Schoon, P.H.N. de With, “Supportive automatic annotation of early esophageal cancer using local Gabor and color features”, Neurocomputing, vol. 144, November 2014, pp. 92-106, ISSN: 0925-2312, doi:10.1016/j.neucom.2014.02.066