Feature Extraction by Image Transformations for Cell Image Classification

When it comes to decision-making in the medical context, opaque black box models, as accurate as they may be, have still not caught on in clinical practice. To present an alternative to the ever-growing convolutional neural networks, in this paper, we focus on classical image transformations, which are used in established compression methods. Using the example of modern high-throughput cytology by means of label-free quantitative phase microscopy, we want to evaluate the transformed cell features using comprehensible classifiers. We can show that the resulting transparent pipelines are roughly comparable to previous work using classical or black box models. The optimized methods generalize somewhat worse on unknown cells and outliers but represent a good trade-off between transparency and performance.