Mean P(best > random) for conferences that took place in the indicated years, for both the Scholar and Scopus datasets.

<p>Mean P(best > random) for conferences that took place in the indicated years, for both the Scholar and Scopus datasets.</p

P(best > random) for the two datasets analyzed herein.

<p>The entry “all” indicates the overall P(best > random). The error bar indicates the 95% confidence interval and the point at the center indicates the mean value of the probability that a best paper will receive more citations than a random non-best paper. The entries 2005 to 2011 indicate the mean and confidence interval of P(best > random) for conferences that took place in those years.</p

Specifics of the Scopus data.

<p>The first figure in each cell is the number of non-best papers in the conference instance. The figure in parenthesis is the number of best papers.</p

Specifics of the Google Scholar data.

<p>The first figure in each cell is the number of non-best papers in the conference instance. The figure in parenthesis is the number of best papers.</p

Composition of the cross-dataset training and testing.

<p>*The annotations SH and DH are added to form the training set in DR1, summing 180 images due to the overlap.</p

Accuracy for Training with DR1, Testing with Messidor.^*

<p>*AUC in %; best accuracy is shown in bold.</p

State of the art for the detection of bright lesions.

<p>*HEI-MED dataset.</p><p>**MESSIDOR dataset.</p><p>***ROC dataset.</p><p>****AUC obtained for training on HEI-MED dataset and test on Messidor dataset.</p

Regions of interest (dashed black regions) and the points of interest (blue circles).

<p>Points of interest falling within the regions marked by the specialist are considered for creating the class-aware codebook – half of the codebook is learned from local features sampled inside the regions marked as lesions, and half the codebook is learned from local features outside those regions.</p

State of the art for the detection of red lesions.

<p>*MESSIDOR dataset.</p><p>**ROC dataset.</p

The BoVW model illustrated as a matrix.

<p>The figure highlights the relationship between the low-level features <b>x</b><i>_j</i>, the codewords <b>c</b><i>_m</i> of the visual dictionary, the encoded features <i>α_m</i>, the coding function <i>f</i> and the pooling function <i>g</i>.</p