Fig 2. Bias in baseline algorithm predictions can be corrected using linear transformation.

The baseline algorithm tends to over-estimate low egg counts and under-estimate high egg counts with the error polarity and magnitude related to the mean of the training set. (A-B) error associated with egg counts (y-axis) from a set of vials containing transparent (A) or opaque (B) media selected to span a broad range of ground-truth values (x-axis). There is a significant linear correlation with an x-axis intercept close to the mean of the distribution in these two datasets, confidence interval (95%) is indicated as dashed coloured lines. (C) Through calculating bias correction during training it is possible to correct the baseline predictive estimate for vial counts. The accuracy was improved for 10 different datasets when bias correction was applied, depicted in panel (transparent media; a-e) and (opaque media; f-j). (D) Summary comparison of transparent media datasets without correction (light-blue) and with bias correction (dark-blue) and of opaque media without correction (yellow-orange) and with correction (dark-orange). Bias correction increased accuracy in both media types. Each dataset was captured independently and contains 8 vial images with the exception of e and j which represent the vials depicted in A and B repectively. A Leave-one-out cross-validation strategy was performed for each vial (7 in and 1 out). Accuracy represents average of five statistical replicates, error bars represent SE (n = 5, statistical replicates); * represents P<0.05, Mann-Whitney one-tailed test.