Investigating Skin Cancer with Unsupervised Learning

Sammanfattning: Skin cancer is one of the most commonly diagnosed cancers in the world. Diagnosis of skin cancer is commonly performed by analysing skin lesions on the patient’s body. Today’s medical diagnostics use a established set of labels for different types of skin lesions. Another way of categorising skin lesions could be to let a computer perform the analysis without any prior knowledge of the data, where the data is a data set of skin lesion images. This categorisation could then be compared to the already existing medical labels assigned to each image. This categorisation and comparison could provide insight into underlying structures of skin lesion data. To investigate this, three unsupervised learning algorithms; K-means, agglomerative clustering, and spectral clustering, have been used to produce cluster partitionings on a data set of skin lesion images. We found no clear cluster partitionings and no connection to the already existing medical labels. The highest scoring partitioning was produced by spectral clustering when the number of clusters was set to two. Further investigation into the structure of this partitioning revealed that one cluster contained essentially every image. Although relatively low, the score does indicate that the underlying structure may be best represented by a single cluster.