COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning

The COVID-19 pandemic continues to rage on, with multiple waves causing substantial harm to health and economies around the world. Motivated by the use of CT imaging at clinical institutes around the world as an effective complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a neural network tailored for detection of COVID-19 cases from chest CT images as part of the open source COVID-Net initiative. However, one potential limiting factor is restricted quantity and diversity given the single nation patient cohort used. In this study, we introduce COVID-Net CT-2, enhanced deep neural networks for COVID-19 detection from chest CT images trained on the largest quantity and diversity of multinational patient cases in research literature. We introduce two new CT benchmark datasets, the largest comprising a multinational cohort of 4,501 patients from at least 15 countries. We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2, with the results for select cases reviewed and reported on by two board-certified radiologists with over 10 and 30 years of experience, respectively. The COVID-Net CT-2 neural networks achieved accuracy, COVID-19 sensitivity, PPV, specificity, and NPV of 98.1%/96.2%/96.7%/99%/98.8% and 97.9%/95.7%/96.4%/98.9%/98.7%, respectively. Explainability-driven performance validation shows that COVID-Net CT-2's decision-making behaviour is consistent with radiologist interpretation by leveraging correct, clinically relevant critical factors. The results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CT-2 and benchmark datasets will continue to enable researchers, clinicians, and citizen data scientists alike to build upon them.Comment: 15 page

Identificação de objetos para veículos autónomos com base em aprendizagem automática

Autonomous driving is one of the most actively researched fields in artificial intelligence. The autonomous vehicles are expected to significantly reduce the road accidents and casualties one day when they become sufficiently mature transport option. Currently much effort is focused to prove the concept of autonomous vehicles that is based on a suit of sensors to observe their surroundings. In particular, camera and LiDAR are researched as an efficient combination of sensors for on-line object identification on the road. 2D object identification is an already established field in Computer Vision. The successful application of Deep Learning techniques has led to 2D vision with Human-level accuracy. However, for a matter of improved safety more advanced approaches suggest that the vehicle should not rely on a single class of sensors. LiDAR has been proposed as an additional sensor, particularly due to its 3D vision capability. 3D vision relies on LiDAR captured data to recognize objects in 3D. However, in contrast to the 2D object identifi- cation, 3D object detection is a relatively immature field and still has many challenges to overcome. In addition, LiDARs are expensive sensors, which makes the acquisition of data required for training 3D object recognition techniques expensive tasks as well. In this context, this Master's thesis has the major goal to further facilitate the 3D object identification for autonomous vehicles based on Deep Learning (DL). The specific contributions of the present work are the following. First, a comprehensive overview of the state of the art Deep Learning architectures for 3D object identification based on Point Clouds. The purpose of this overview is to understand how to better approach such a problem in the context of autonomous driving. Second, synthetic but realistic Lidar captured data was generated in the GTA V virtual environment. Tools were developed to convert the generated data into the KITTI dataset format, which has become standard in 3D object detection techniques for autonomous driving. Third, some of the overviewed 3D object identification DL architectures were evaluated with the generated data. Though their performance with the generated data was worse than with the original KITTI data, the models were still able to correctly process the synthetic data without being retrained. The future benefit of this work is that the models can be further trained with home-made data and varying testing scenarios. The implemented GTA V mod has proved to be capable of providing rich, well-structured and compatible datasets with the state of the art 3D object identification architectures. The developed tool is publicly available and we hope it will be useful in advancing 3D object identification for autonomous driving, as it removes the dependency from datasets provided by a third party.Condução autónoma é uma das áreas mais ativamente estudadas em inteligência artificial. É esperado que os veículos autónomos reduzam significativamente os acidentes rodoviários e vitimas mortais quando se tornarem suficientemente maturos como opção de transporte. Atualmente, muitos dos esforços estão focados na prova de conceito de veículos autónomos serem baseados num conjunto de sensores que observam o ambiente em redor. Em particular, a camara e o LiDAR são estudados como sendo uma combinação eficiente de sensores para realização de identificação de objectos on-line nas estradas. Identificação de objetos 2D é uma área de estudo já estabelecida no campo de Computação Visual. O sucesso na aplicação de técnicas de Deep Learning levou a que a visão 2D atingisse uma precisão ao nível Humano. No entanto, de forma a melhorar a segurança, abordagens mais avançadas sugerem que o veículo não deve depender de uma única classe de sensores. O LiDAR foi proposto como sendo um sensor adicional, particularmente devido à sua capacidade de visão 3D. Visão 3D depende dos dados capturados pelo LiDAR para reconhecer objetos em 3D. No entanto, em contraste com a identificação de objetos 2D, a identificação de objetos 3D é um campo de estudos relativamente imaturo e ainda possui muitos desafios para ultrapassar. Adicionalmente, LiDARs são sensores dispendiosos, o que também torna a aquisição de dados necessários para o treino de técnicas de reconhecimento de objetos 3D mais cara. Neste contexto, esta tese de Mestrado tem como objetivo principal facilitar a identificação de objetos 3D, baseada em Deep Learning (DL), para veículos autónomos. As contribuições especificas deste trabalho são as seguintes. Primeiro, uma visão global compreensiva do estado de arte relativo _as arquiteturas Deep Learning para identificação de objetos 3D baseadas em point clouds. O propósito desta visão global é para perceber como melhor abordar este tipo de problema no contexto de condução autónoma. Segundo, foi gerado um dataset sintético, mas realista, com dados capturados por um LiDAR no ambiente virtual do GTA V. Foram desenvolvidas ferramentas para converter os dados gerados no formato do dataset do KITTI, que se tornou num standard para avaliação de técnicas de deteção de objetos 3D para condução autónoma. Terceiro, algumas das arquiteturas DL de identificação de objetos 3D revistas foram avaliadas com o dataset gerado. Apesar da sua performance com o dataset gerado ter sido pior que os resultados no dataset original do KITTI, os models chegaram a conseguir processar corretamente os dados sintéticos sem serem retreinados. O benefício futuro deste trabalho consiste nos modelos poderem ser adicionalmente treinados com dados produzidos localmente e testados em cenários variados. O mod do GTA V implementado provou ser capaz de fornecer datasets ricos, bem estruturados e compatíveis com o estado de arte em arquiteturas de identificação de objetos 3D. A ferramenta desenvolvida está disponível publicamente e esperamos que seja útil para o avanço da identificação de objetos 3D para condução autónoma, já que remove a dependência de datasets fornecidos por terceiros.Mestrado em Engenharia de Computadores e Telemátic

Investigating Scene Understanding for Robotic Grasping: From Pose Estimation to Explainable AI

In the rapidly evolving field of robotics, the ability to accurately grasp and manipulate objects—known as robotic grasping—is a cornerstone of autonomous operation. This capability is pivotal across a multitude of applications, from industrial manufacturing automation to supply chain management, and is a key determinant of a robot's ability to interact effectively with its environment. Central to this capability is the concept of scene understanding, a complex task that involves interpreting the robot's environment to facilitate decision-making and action planning. This thesis presents a comprehensive exploration of scene understanding for robotic grasping, with a particular emphasis on pose estimation, a critical aspect of scene understanding. Pose estimation, the process of determining the position and orientation of objects within the robot's environment, is a crucial component of robotic grasping. It provides the robot with the necessary spatial information about the objects in the scene, enabling it to plan and execute grasping actions effectively. However, many current pose estimation methods provide relative pose compared to a 3D model, which lacks descriptiveness without referencing the 3D model. This thesis explores the use of keypoints and superquadrics as more general and descriptive representations of an object's pose. These novel approaches address the limitations of traditional methods and significantly enhance the generalizability and descriptiveness of pose estimation, thereby improving the overall effectiveness of robotic grasping. In addition to pose estimation, this thesis briefly touches upon the importance of uncertainty estimation and explainable AI in the context of robotic grasping. It introduces the concept of multimodal consistency for uncertainty estimation, providing a reliable measure of uncertainty that can enhance decision-making in human-in-the-loop situations. Furthermore, it explores the realm of explainable AI, presenting a method for gaining deeper insights into deep learning models, thereby enhancing their transparency and interpretability. In summary, this thesis presents a comprehensive approach to scene understanding for robotic grasping, with a particular emphasis on pose estimation. It addresses key challenges and advances the state of the art in this critical area of robotics research. The research is structured around five published papers, each contributing to a unique aspect of the overall study