Precise skin lesion classification is still challenging due to two problems, i.e., (1) inter-class similarity and intra-class variation of skin lesion images, and (2) the weak generalization ability of single Deep Convolutional Neural Network trained with limited data. Therefore, we propose a Global-Part Convolutional Neural Network (GP-CNN) model, which treats the fine-grained local information and global context information with equal importance. The Global-Part model consists of a Global Convolutional Neural Network (G-CNN) and a Part Convolutional Neural Network (P-CNN). Specifically, the G-CNN is trained with downscaled dermoscopy images, and is used to extract the global-scale information of dermoscopy images and produce the Classification Activation Map (CAM). While the P-CNN is trained with the CAM guided cropped image patches and is used to capture local-scale information of skin lesion regions. Additionally, we present a data-transformed ensemble learning strategy, which can further boost the classification performance by integrating the different discriminant information from GP-CNNs that are trained with original images, color constancy transformed images, and feature saliency transformed images, respectively. The proposed method is evaluated on the ISIC 2016 and ISIC 2017 Skin Lesion Challenge (SLC) classification datasets. Experimental results indicate that the proposed method can achieve the state-of-the-art skin lesion classification performance (i.e., an AP value of 0.718 on the ISIC 2016 SLC dataset and an Average Auc value of 0.926 on the ISIC 2017 SLC dataset) without any external data, compared with other current methods which need to use external data.

The importance of force sensing technologies was recognized in the 1970s. Since then, multidimensional microelectromechanical systems (MEMS)/micro force/moment (F/M) sensors have permeated a wide variety of products. A multidimensional MEMS/micro-F/M sensor can measure the tangential force terms along x-, y-, and z-axis (F _x , F _y and F _z ) as well as the moments terms about x-, y-, and z-axis (M _x , M _y and M _z ) simultaneously with micro-Newton and nano-Newtonmeter resolution. This paper presents an overview of MEMS/micro-F/M sensors. This field is critical to many biomedical applications, materials science, industrial automation, dimension measurements in microcomponents, and nanomanufacturing applications, and has attracted great activity in the past 15-25 years. The evaluation of different F/M sensing principles, recent advances in various designs, and their significance and limitations are analyzed through specific examples. Furthermore, current challenges and new area for future applications of micro-F/M sensing technology have been identified.