Multiple instance learning (MIL) is a typical weakly-supervised learning method where the label is associated with a bag of instances instead of a single instance. Despite extensive research over past years, effectively deploying MIL remains an open and challenging problem, especially when the commonly assumed standard multiple instance (SMI) assumption is not satisfied. In this paper, we propose a multiple instance learning method based on deep graph convolutional network and feature selection (FS-GCN-MIL) for histopathological image classification. The proposed method consists of three components, including instance-level feature extraction, instance-level feature selection, and bag-level classification. We develop a self-supervised learning mechanism to train the feature extractor based on a combination model of variational autoencoder and generative adversarial network (VAE-GAN). Additionally, we propose a novel instance-level feature selection method to select the discriminative instance features. Furthermore, we employ a graph convolutional network (GCN) for learning the bag-level representation and then performing the classification. We apply the proposed method in the prediction of lymph node metastasis using histopathological images of colorectal cancer. Experimental results demonstrate that the proposed method achieves superior performance compared to the state-of-the-art methods.

This paper studies the secure consensus control of multi-agent systems where some agents are injected into malicious attacks and compromised. We focus on the distributed detection and identification of attacks on agents and the realization of consensus of agents against attacks. Firstly, we design monitors that can perform precise distributed attack detection. Then, we propose a distributed attack identification algorithm by constructing distributed observers. After that, a cooperative controller is proposed which ensures the normal control of systems by means of isolating the agents which are attacked. Finally, we validate our results through simulation examples.

In traditional localization systems, it is required that moving object carries a device to transmit or receive signals, and then localization system is able to locate an object based on signal strength it received. In this paper, we propose a new passive localization and tracking approach based on RFID with sparse reference tags, which can estimate the location of moving objects by detecting and analyzing signal strength distribution of target area. We firstly construct a signal fluctuation ellipse model between RFID reader and tag through the experiments, and then present a localization method based on this model. Then a tracking method based on Hidden Markov Model (HMM) is proposed to predict the trajectory of an object in a passive localization system with sparse reference tag. The experimental results show that our method not only reduces the computation complexity and cost but also ensures the accuracy of localization and tracking.