Melanoma is one of the most lethal forms of skin cancer. It occurs on the skin surface and develops from cells known as melanocytes. The same cells are also responsible for benign lesions commonly known as moles, which are visually similar to melanoma in its early stage. If melanoma is treated correctly, it is very often curable. Currently, much research is concentrated on the automated recognition of melanomas. In this paper, we propose an automated melanoma recognition system, which is based on deep learning method combined with so called hand-crafted RSurf features and Local Binary Patterns. The experimental evaluation on a large publicly available dataset demonstrates high classification accuracy, sensitivity, and specificity of our proposed approach when it is compared with other classifiers on the same dataset.

Visual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics.

Three dimensional electron microscopy is becoming a very data-intensive field in which vast amounts of experimental images are acquired at high speed. To manage such large-scale projects, we had previously developed a modular workflow system called Scipion (de la Rosa-Trevín et al., 2016). We present here a major extension of Scipion that allows processing of EM images while the data is being acquired. This approach helps to detect problems at early stages, saves computing time and provides users with a detailed evaluation of the data quality before the acquisition is finished. At present, Scipion has been deployed and is in production mode in seven Cryo-EM facilities throughout the world.