  The Deep Learning revolution has enabled groundbreaking achievements in
recent years. From breast cancer detection to protein folding, deep learning
algorithms have been at the core of very important advancements. However, these
modern advancements are becoming more and more data-hungry, especially on
labeled data whose availability is scarce: this is even more prevalent in the
medical context. In this work, we show how active learning could be very
effective in data scarcity situations, where obtaining labeled data (or
annotation budget is very limited). We compare several selection criteria
(BALD, MeanSTD, and MaxEntropy) on the ISIC 2016 dataset. We also explored the
effect of acquired pool size on the model's performance. Our results suggest
that uncertainty is useful to the Melanoma detection task, and confirms the
hypotheses of the author of the paper of interest, that \textit{bald} performs
on average better than other acquisition functions. Our extended analyses
however revealed that all acquisition functions perform badly on the positive
(cancerous) samples, suggesting exploitation of class unbalance, which could be
crucial in real-world settings. We finish by suggesting future work directions
that would be useful to improve this current work. The code of our
implementation is open-sourced at
\url{https://github.com/bonaventuredossou/ece526_course_project}
