A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification

Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Effective business planning requires seamless access and intelligent analysis of information in its totality to allow the business planner to gain enhanced critical business insights for decision support. Current business planning tools provide insights from structured business data (i.e. sales forecasts, customers and products data, inventory details) only and fail to take into account unstructured complementary information residing in contracts, reports, user\u27s comments, emails etc. In this article, a planning support system is designed and developed that empower business planners to develop and revise business plans utilizing both structured data and unstructured information conjointly. This planning system activity model comprises of two steps. Firstly, a business planner develops a candidate plan using planning template. Secondly, the candidate plan is put forward to collaborating partners for its revision interleaving deliberation. Planning interleaving deliberation activity in the proposed framework enables collaborating planners to challenge both a decision and the thinking that underpins the decision in the candidate plan. The planning system is modeled using situation calculus and is validated through a prototype development

Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection

Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfer learning, which fine-tunes a source model after replacing its classification layers, PRT retains the original classification layers and updates the representation layers through an unsupervised pre-text task. The task is performed with (original, not synthetic) medical images, without utilizing any annotations. This enables representation transfer with a large amount of training data. This high-fidelity representation transfer allows us to use the resulting model as a more effective feature extractor. Moreover, we can also subsequently perform the traditional transfer learning with this model. We devise a collaborative representation based classification layer for the case when we leverage the model as a feature extractor. We fuse the output of this layer with the predictions of a model induced with the traditional transfer learning performed over our pre-text transferred model. The utility of our technique for limited and imbalanced data classification problem is demonstrated with an extensive five-fold evaluation for three large-scale models, tested for five different class-imbalance ratios for CT based COVID-19 detection. Our results show a consistent gain over the conventional transfer learning with the proposed method.Comment: Best paper at IVCN

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future

Perspectives on wider integration of the health-assistive smart home

Most older adults desire to be as independent as possible and remain living in their ancestral home as they age. Aging-in-place maximizes the independence of older adults, enhancing their wellbeing and quality of life while decreasing the financial burden of residential care costs. However, due to chronic disease, multimorbidity, and age-related changes, appropriate conditions are required to make aging-in-place possible. Remote monitoring with smart home technologies could provide the infrastructure that enables older adults to remain living independently in their own homes safely. The health-assistive smart home shows great promise, but there are challenges to integrating smart homes on a larger scale. The purpose of this discussion paper is to propose a Design Thinking (DT) process to improve the possibility of integrating a smart home for health monitoring more widely and making it more accessible to all older adults wishing to continue living independently in their ancestral homes. From a nursing perspective, we discuss the necessary stakeholder groups and describe how these stakeholders should engage to accelerate the integration of health smart homes into real-world settings

A proactive event-driven approach for dynamic QoS compliance in cloud of things

Cloud-of-things service providers use various descriptions languages to describe Quality of Service (QoS) attributes. However, existing modelling approaches provide support for modelling static QoS attributes only and lack features to model and reason with dynamic QoS attributes such as response time and availability. This paper presents an event-based approach for monitoring dynamic QoS values and their compliance by modelling the behavior of QoS attributes using an Event Calculus (EC) based framework. The logic based reasoning is then performed to proactively identify the possible QoS violations in future

Model synthesis and stochastic automated verification of systems-of-systems dynamic architectures

Software intensive Systems-of-Systems (SoS) are complex alliances of autonomous Constituent Systems (CSs) formed at a large scale to achieve a common objective. As such the CSs are operationally and managerially independent and geographically dispersed which generate emergent behaviors to achieve SoS missions through collective dynamics. Therefore, architectural modeling and analysis of a resulting SoS is pivotal to avoid stochastic architectural arrangements that can lead to undesired behaviors, systems outages, losses and non-conformance of core Quality Attributes (QAs) such as performance and reliability. In this research, we propose a formally founded approach for stochastic synthesis and automated verification of SoS architectural models to predict the impact of dynamic architectural changes on QAs at runtime. At first, we provide Hybrid Stochastic Formalism (HSF) based on Process Algebras (PAs) to model the stochastic SoS software architecture. At the architectural level, non-determinism is dealt with by treating HSF as Markov Decision Process (MDP). The SoS modeled with MDP is then verified against certain system properties using model checking through Probabilistic Computation Tree Logic (PCTL) operators. The effectiveness of our approach is evaluated through a fire monitoring and emergency response SoS to predict the impact of dynamic reconfiguration on QAs. The experimental results show that our method helps to assess different architectural configurations that support design choices to achieve missions without compromising quality