||As soon as it became possible to scan and load medical or biomedical images into a computer, researchers have built systems for automated analysis. Since the inception of deep learning, research has seen a shift from rule-based, problem-specific solutions to increasingly generic, problem agnostic methods that rely on training data. In particular, convolutional neural networks have rapidly become a primary choice for many computer-aided detection (CAD) workflows due to its astonishing results in multiple tasks, e.g., classification, denoising, etc. Given the prevalence of deep neural networks, I will highlight the applications of neural networks in medical and biomedical image analysis for two tasks. Firstly, classification of pulmonary nodules in CT images and classification of cilia in low-magnification TEM images. Secondly, denoising of short exposure high-magnification TEM images for ultrastructural enhancement.