Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.

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Abstract:The extraction of urban water bodies from high-resolution remote sensing images, which has been a hotspot in researches, has drawn a lot of attention both domestic and abroad. A challenging issue is to distinguish the shadow of high-rise buildings from water bodies. To tackle this issue, we propose the automatic urban water extraction method (AUWEM) to extract urban water bodies from high-resolution remote sensing images. First, in order to improve the extraction accuracy, we refine the NDWI algorithm. Instead of Band2 in NDWI, we select the first principal component after PCA transformation as well as Band1 for ZY-3 multi-spectral image data to construct two new indices, namely NNDWI1, which is sensitive to turbid water, and NNDWI2, which is sensitive to the water body whose spectral information is interfered by vegetation. We superimpose the image threshold segmentation results generated by applying NNDWI1 and NNDWI2, then detect and remove the shadows in the small areas of the segmentation results using object-oriented shadow detection technology, and finally obtain the results of the urban water extraction. By comparing the Maximum Likelihood Method (MaxLike) and NDWI, we find that the average Kappa coefficients of AUWEM, NDWI and MaxLike in the five experimental areas are about 93%, 86.2% and 88.6%, respectively. AUWEM exhibits lower omission error rates and commission error rates compared with the NDWI and MaxLike. The average total error rates of the three methods are about 11.9%, 18.2%, and 22.1%, respectively. AUWEM not only shows higher water edge detection accuracy, but it also is relatively stable with the change of threshold. Therefore, it can satisfy demands of extracting water bodies from ZY-3 images.

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