Figure 7.

Preliminary experiments investigating various values for the ${\alpha }_t$ and $\gamma$ parameters in the focal loss equation, highlighting the rationale behind optimized values of ${\alpha }_t=0.25$ and $\gamma =2$, which were also reported as optimized values in Lin et al.⁴⁴ Here, we iterated across ${\alpha }_t=0.25,1,and\mathrm{inverse}\mathrm{class}\mathrm{frequency}$ ("weights") and $\gamma =1.5,2,3and4$. Both (a) and (b) illustrate Bland–Altman plots (top panel) and continuous score histograms (bottom panel), highlighting both repeatability and relative class discrimination across the various parameter choices. In (a), $\gamma$ is held constant, and ${\alpha }_t$ (0.25, inverse class frequency) and the method of reduction (mean, sum) are iterated. In (b), ${\alpha }_t$ and the method of reduction are held constant, while $\gamma$ (1.5, 2, 3, 4) is iterated. Overall, the results indicate that increasing $\gamma$ leads to improved repeatability (as indicated by the narrower 95% limit of agreement (LoA) on the Bland Altman plot) but slightly poorer class discrimination (as indicated by the narrower score range in both the Bland Altman plot and the histogram); changing ${\alpha }_t$ and/or the method of reduction has relatively less effect on repeatability and class discrimination. The best overall balance between the two is achieved with ${\alpha }_t=0.25$ and $\gamma =2$, consistent with Lin et al.⁴⁴.