Using Deep Learning to Automate the Diagnosis of Skin Melanoma

Machine learning and image processing techniques have been widely implemented in the field of medicine to help accurately diagnose a multitude of medical conditions. The automated diagnosis of skin melanoma is one such instance. However, a majority of the successful machine learning models that have been implemented in the past have used deep learning approaches where only raw image data has been utilized to train machine learning models, such as neural networks. While they have been quite effective at predicting the condition of these lesions, they lack key information about the images, such as clinical data, and features that medical professionals consistently rely on for diagnosis. This research project will explore methods to enhance machine learning models with three drastically different skin melanoma datasets, each with their own set of unique challenges. Various preprocessing techniques, machine learning models, and feature extraction methods will be compared to determine the most optimal approach for each dataset. In addition, time and space complexities of the approaches will also be analyzed in order to minimize resource consumption without causing major performance degradation to the model

Incorporating Demographic Structure and Variable Interaction Types into Community Assembly Models

Theoretical studies of ecological food webs have allowed ecologists to remove the constraints of specific location and timescales from their study of ecological communities; food webs are generally complex and thus empirical study is difficult. Further, this theoretical approach allows ecologists to compare ecological processes and outcomes across any possible food web structures. However, these simulated communities are only as useful as the model from which they were constructed. Modifying existing considerations in these models, and generating new ones, are the jobs of theoretical ecologists that seek to achieve the shared goal of a majority of simulations: representation of real natural systems. However, there are many different models that have been developed, all by individuals with varying approaches to achieving biologically realistic results. The difficulty of comparing and combining every single model is not a feat any one study or model can be expected to accomplish. Instead, the paired studies presented here seek to examine two ubiquitous features of ecological communities that are often omitted from food web models: stage-structured interactions, and networks of varied ecological interaction types. By generating the results of these differing models, the effects of combining approaches on the assembly and stability of communities can be examined