The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain patterns and association neurological diseases with these variations. However, conventional D-FCN is essentially low-order network, which only reflects pairwise interaction pattern between regions thus overlooking high-order interactions a...

Besides the complex nature of colonoscopy frames with intrinsic frame formation artefacts such as light reflections and diversity polyp types/shapes, publicly available segmentation training datasets are limited, small imbalanced. In this case, automated using a deep neural network remains an open challenge due to overfitting on datasets. We proposed simple yet effective pipeline that couples (...

Use of neural networks for computer vision, speech recognition, and other applications has exploded in recent years, in part due to their unprecedented performance on a variety of benchmarks. Nonetheless, highthroughput and energy-efficient evaluation of such neural networks, and in particular, convolutional neural networks (CNNs), remains an active field of research. Evaluation of networks is ...

für Naturforschung in cooperation with the Max Planck Society for the Advancement of Science under a Creative Commons Attribution 4.0 International License. Dieses Werk wurde im Jahr 2013 vom Verlag Zeitschrift für Naturforschung in Zusammenarbeit mit der Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. digitalisiert und unter folgender Lizenz veröffentlicht: Creative Commons Namen...

Landslide inventory mapping (LIM) is a key prerequisite for landslide susceptibility evaluation and disaster mitigation. It aims to record the location, size, extent of landslides in each map scale. Machine learning algorithms, such as support vector machine (SVM) random forest (RF), have been increasingly applied detection using remote sensing images recent decades. However, their limitations ...

In fuzzy decision-making problems, existing methods only indicate assessment values but lack the degrees/levels of credibility regarding in alternatives over attributes due to vagueness and uncertainty human cognitions/judgments for complicated problems. Therefore, degree value shows its importance necessity problem. To enhance values, should be closely related their measures, which make inform...

Most of existing salient object detection models are based on fully convolutional network (FCN), which learn multi-scale/level semantic information through layers to obtain high-quality predicted saliency maps. However, convolution is locally interactive, it difficult capture remote dependencies, and FCN-based methods suffer from coarse boundaries. In this paper, solve these problems, we propos...

Deep convolutional neural networks (CNNs) have outperformed existing object recognition and detection algorithms. On the other hand satellite imagery captures scenes that are diverse. This paper describes a deep learning approach that analyzes a geo referenced satellite image and efficiently detects built structures in it. A Fully Convolution Network (FCN) is trained on low resolution Google ea...

Locating lung field is a critical and fundamental processing stage in the automated analysis of chest radiographs (CXRs) for pulmonary disorders. During routine examination CXRs, using both frontal lateral CXRs can benefit clinical diagnosis cardiothoracic diseases. However, accurate segmentation fields on still challenging due to blurry boundary poor generalization ability models. Existing dee...