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Machine learning (ML) based skin cancer detection tools are an example of a transformative medical technology that could potentially democratize early detection for skin cancer cases for everyone. However, due to the dependency of datasets for training, ML based skin cancer detection always suffers from a systemic racial bias. Racial communities and ethnicity not well represented within the training datasets will not be able to use these tools, leading to health disparities being amplified. Based on empirical observations we posit that skin cancer training data is biased as it’s dataset represents mostly communities of lighter skin tones, despite skin cancer being far more lethal for people of color. In this paper we use domain adaptation techniques by employing CycleGANs to mitigate racial biases existing within state of the art machine learning based skin cancer detection tools by adapting minority images to appear as the majority. Using our domain adaptation techniques to augment our minority datasets, we are able to improve the accuracy, precision, recall, and F1 score of typical image classification machine learning models for skin cancer classification from the biased 50% accuracy rate to a 79% accuracy rate when testing on minority skin tone images. We evaluate and demonstrate a proof-of-concept smartphone application. 

Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, two-step deep learning approach for underwater image dehazing and colour correction. In iDehaze, we leverage computer graphics to physically model light propagation in underwater conditions. Specifically, we construct a three-dimensional, photorealistic simulation of underwater environments, and use them to gather a large supervised training dataset. We then train a deep convolutional neural network to remove the haze in these images, then train a second network to transform the colour space of the dehazed images onto a target domain. Experiments demonstrate that our two-step iDehaze method is substantially more effective at producing high-quality underwater images, achieving state-of-the-art performance on multiple datasets. Code, data and benchmarks will be open sourced.