Detecting objects in remote sensing images is essential for intelligent interpretation. Although deep neural networks have made significant progress in recent years, they often struggle with complex backgrounds in remote sensing images, which can lead to inaccurate detection. To tackle this problem, a self-supervised adaptive fusion network (SSAFN) has been developed. The SSAFN includes an adaptive fusion module (AFM) and a self-supervised task module (SSTM). The AFM mainly fuses the deep semantic information to the shallow features with appropriate weights to enhance the semantic information of the shallow features. The SSTM is mainly to constrain the AFM through self-supervised tasks to fulfill the function similar to the attention mechanism: to make the AFM enhance the target feature representation and suppress the background information. The SSAFN reduces the impact of complex backgrounds on object representation, resulting in better detection results for various types of objects such as buildings, ships, and more. The proposed method has been tested on various datasets and has not only improved the detection accuracy for different types of objects but also enhanced the performance of popular object detection algorithms.

EHRs contain the patient’s historical disease information, and a natural idea is to predict the patient’s disease or diagnose the patient based on EHRs. However, existing deep learning models using EHRs are not satisfactory in solving several key challenges: 1) most of the existing methods are lack of priori knowledge assistance during model learning; and 2) higher-order relationships among diseases and patients are not explored sufficiently. To address these issues, in this paper we propose a hypergraph-based deep learning model for disease prediction, namely EHR2HG. First, we propose to utilize the existing disease classification information to give the model a better initialization condition. Second, by constructing hypergraphs, we consider all of the patients together instead of separate individuals, so our models can model entity relationships such as comorbidities or patient class groups. We have evaluated the proposed model on a real-world EHRs dataset and the results demonstrated that EHR2HG can achieve comparable and better performance than several state-of-the-art baseline methods.

Building detection in remote sensing images plays an important role in applications such as urban management and urban planning. Recently, convolutional neural network (CNN) based methods which benefits from the popularity of large-scale datasets have achieved good performance for object detection. To our best knowledge, there is no large-scale remote sensing image dataset specially build for building detection. Existing building datasets are in small size and lack of diversity, which hinder the development of building detection. In this paper, we present a large-scale high-resolution building dataset named 5M-Building after the number of samples in the dataset. The dataset consists of more than 10 thousand images all collected from GaoFen-2 with a spatial resolution of 0.8 meter. We also present a baseline for the dataset by evaluating three state of the art CNN based detectors. The experiments demonstrate that it is great challenge to accurately detect various buildings from remote sensing images. We hope the 5M-Building dataset will facilitate the research on building detection.

Skin colour detection plays an important role in image processing and computer vision. Selection of a suitable colour space is one key issue. The question that which colour space is most appropriate for pixel‐wise skin colour detection is not yet concluded. In this study, a pixel‐wise skin colour detection method is proposed based on the flexible neural tree (FNT) without considering the problem of selecting a suitable colour space. A FNT‐based skin model is constructed by using large skin data sets which identifies the important components of colour spaces automatically. Experimental results show improved accuracy and false positive rates (FPRs). The structure and parameters of FNT are optimised via genetic programming and particle swarm optimisation algorithms, respectively. In the experiments, nine FNT skin models are constructed and evaluated on features extracted from RGB, YCbCr, HSV and CIE‐Lab colour spaces. The Compaq and ECU datasets are used for constructing FNT‐based skin model and evaluating its performance compared with other skin detection methods. Without extra processing steps, the authors method achieves state of the art performance in skin pixel classification and better performance in terms of accuracy and FPRs.