Skin Lesion Analysis By Multi-Target Deep Neural Networks.

Automatic skin lesion analysis involves two critical steps: lesion segmentation and lesion classification. In this work, we propose a novel multi-target deep convolutional neural network (DCNN) to simultaneously tackle the problem of segmentation and classification. Based on U-Net and GoogleNet, a single model is constructed with three different targets of both lesion segmentation and two independent binary lesion classifications (i.e., melanoma detection and seborrheic keratosis identification), aiming to explore the differences and commonalities over different target models. We conduct experiments on dermoscopic images from the International Skin Imaging Collaboration (ISIC) 2017 Challenge. Results of our multi-target DCNN model demonstrates superiority over single model with one target only (such as U-net or GoogleNet), indicating its learning efficiency and potential for application in automatic skin lesion diagnosis. To the best of our knowledge, this work is the first demonstration for a single end-to-end deep neural network model that simultaneously handle both segmentation and classification in the field of skin lesion analysis.

Markov Chain Monte Carlo (MCMC) Method for Studying Magnetic Behaviors in Trinuclear Cobalt(II) Compound.

A CoII coordination polymer built from a mixed azide and zwitterionic pyridinium ions and its temperature-dependent magnetic properties are described. We used the Markov chain Monte Carlo (MCMC) method to fit the data, and found the following results: (1) there are strong correlations between the model parameters; (2) the data at above 28 K are well fitted by the magnetism model.