Emanuele Pesce

Bio: Emanuele Pesce is an academic researcher from University of Warwick. The author has contributed to research in topics: Reinforcement learning & Network theory. The author has an hindex of 5, co-authored 12 publications receiving 248 citations. Previous affiliations of Emanuele Pesce include King's College London & University of Salerno.

Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks

Abstract: An artificial intelligence system, developed on a data set of 470e388 adult chest radiographs, is able to interpret and prioritize abnormal radiographs with critical or urgent findings.

Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication

Abstract: Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for multi-agent training using deep deterministic policy gradients that enables concurrent, end-to-end learning of an explicit communication protocol through a memory device. During training, the agents learn to perform read and write operations enabling them to infer a shared representation of the world. We empirically demonstrate that concurrent learning of the communication device and individual policies can improve inter-agent coordination and performance in small-scale systems. Our experimental results show that the proposed method achieves superior performance in scenarios with up to six agents. We illustrate how different communication patterns can emerge on six different tasks of increasing complexity. Furthermore, we study the effects of corrupting the communication channel, provide a visualisation of the time-varying memory content as the underlying task is being solved and validate the building blocks of the proposed memory device through ablation studies.

Classifying HCP task-fMRI networks using heat kernels

Abstract: Network theory provides a principled abstraction of the human brain: reducing a complex system into a simpler representation from which to investigate brain organisation. Recent advancement in the neuroimaging field are towards representing brain connectivity as a dynamic process in order to gain a deeper understanding of the interplay between functional modules for efficient information transport. In this work, we employ heat kernels to model the process of energy diffusion in functional networks. We extract node-based, multi-scale features which describe the propagation of heat over 'time' which not only inform the importance of a node in the graph, but also incorporate local and global information of the underlying geometry of the network. As a proof-of-concept, we test the efficacy of two heat kernel features for discriminating between motor and working memory functional networks from the Human Connectome Project. For comparison, we also classified task networks using traditional network metrics which similarly provide rankings of node importance. In addition, a variant of the Smooth Incremental Graphical Lasso Estimation algorithm was used to estimate non-sparse, precision matrices to account for non-stationarity in the time series. We illustrate differences in heat kernel features between tasks, and also between regions of the brain. Using a random forest classifier, we showed heat kernel metrics to capture intrinsic properties of functional networks that serve well as features for task classification.

Improving Coordination in Multi-Agent Deep Reinforcement Learning through Memory-driven Communication.

Abstract: Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with a task requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for multi-agent training using deep deterministic policy gradients that enables the concurrent, end-to-end learning of an explicit communication protocol through a memory device. During training, the agents learn to perform read and write operations enabling them to infer a shared representation of the world. We empirically demonstrate that concurrent learning of the communication device and individual policies can improve inter-agent coordination and performance, and illustrate how different communication patterns can emerge for different tasks.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

Abstract: Background Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.

A survey and critique of multiagent deep reinforcement learning

Abstract: Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Additionally, we complement the overview with a broader analysis: (i) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL (e.g., implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (e.g., RL and MAL) in a joint effort to promote fruitful research in the multiagent community.

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification.

Abstract: This paper considers the task of thorax disease classification on chest X-ray images Existing methods generally use the global image as input for network learning Such a strategy is limited in two aspects 1) A thorax disease usually happens in (small) localized areas which are disease specific Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN) AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch Specifically, we first learn a global CNN branch using global images Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image The local region is used for training a local CNN branch Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch The Comprehensive experiment is conducted on the ChestX-ray14 dataset We first report a strong global baseline producing an average AUC of 0841 with ResNet-50 as backbone After combining the local cues with the global information, AG-CNN improves the average AUC to 0868 While DenseNet-121 is used, the average AUC achieves 0871, which is a new state of the art in the community