Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs

The lack of fine-grained annotations hinders the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs. To that end, we introduce a novel loss function for training convolutional neural networks increasing the \emph{localization confidence} and assisting the overall \emph{disease identification}. The loss leverages both image- and patch-level predictions to generate auxiliary supervision. Rather than forming strictly binary from the predictions as done in previous loss formulations, we create targets in a more customized manner, which allows the loss to account for possible misclassification. We show that the supervision provided within the proposed learning scheme leads to better performance and more precise predictions on prevalent datasets for multiple-instance learning as well as on the NIH~ChestX-Ray14 benchmark for disease recognition than previously used losses

An ontology-driven architecture for data integration and management in home-based telemonitoring scenarios

The shift from traditional medical care to the use of new technology and engineering innovations is nowadays an interesting and growing research area mainly motivated by a growing population with chronic conditions and disabilities. By means of information and communications technologies (ICTs), telemedicine systems offer a good solution for providing medical care at a distance to any person in any place at any time. Although significant contributions have been made in this field in recent decades, telemedicine and in e-health scenarios in general still pose numerous challenges that need to be addressed by researchers in order to take maximum advantage of the benefits that these systems provide and to support their long-term implementation. The goal of this research thesis is to make contributions in the field of home-based telemonitoring scenarios. By periodically collecting patients' clinical data and transferring them to physicians located in remote sites, patient health status supervision and feedback provision is possible. This type of telemedicine system guarantees patient supervision while reducing costs (enabling more autonomous patient care and avoiding hospital over flows). Furthermore, patients' quality of life and empowerment are improved. Specifically, this research investigates how a new architecture based on ontologies can be successfully used to address the main challenges presented in home-based telemonitoring scenarios. The challenges include data integration, personalized care, multi-chronic conditions, clinical and technical management. These are the principal issues presented and discussed in this thesis. The proposed new ontology-based architecture takes into account both practical and conceptual integration issues and the transference of data between the end points of the telemonitoring scenario (i.e, communication and message exchange). The architecture includes two layers: 1) a conceptual layer and 2) a data and communication layer. On the one hand, the conceptual layer based on ontologies is proposed to unify the management procedure and integrate incoming data from all the sources involved in the telemonitoring process. On the other hand, the data and communication layer based on web service technologies is proposed to provide practical back-up to the use of the ontology, to provide a real implementation of the tasks it describes and thus to provide a means of exchanging data. This architecture takes advantage of the combination of ontologies, rules, web services and the autonomic computing paradigm. All are well-known technologies and popular solutions applied in the semantic web domain and network management field. A review of these technologies and related works that have made use of them is presented in this thesis in order to understand how they can be combined successfully to provide a solution for telemonitoring scenarios. The design and development of the ontology used in the conceptual layer led to the study of the autonomic computing paradigm and its combination with ontologies. In addition, the OWL (Ontology Web Language) language was studied and selected to express the required knowledge in the ontology while the SPARQL language was examined for its effective use in defining rules. As an outcome of these research tasks, the HOTMES (Home Ontology for Integrated Management in Telemonitoring Scenarios) ontology, presented in this thesis, was developed. The combination of the HOTMES ontology with SPARQL rules to provide a flexible solution for personalising management tasks and adapting the methodology for different management purposes is also discussed. The use of Web Services (WSs) was investigated to support the exchange of information defined in the conceptual layer of the architecture. A generic ontology based solution was designed to integrate data and management procedures in the data and communication layer of the architecture. This is an innovative REST-inspired architecture that allows information contained in an ontology to be exchanged in a generic manner. This layer structure and its communication method provide the approach with scalability and re-usability features. The application of the HOTMES-based architecture has been studied for clinical purposes following three simple methodological stages described in this thesis. Data and management integration for context-aware and personalized monitoring services for patients with chronic conditions in the telemonitoring scenario are thus addressed. In particular, the extension of the HOTMES ontology defines a patient profile. These profiles in combination with individual rules provide clinical guidelines aiming to monitor and evaluate the evolution of the patient's health status evolution. This research implied a multi-disciplinary collaboration where clinicians had an essential role both in the ontology definition and in the validation of the proposed approach. Patient profiles were defined for 16 types of different diseases. Finally, two solutions were explored and compared in this thesis to address the remote technical management of all devices that comprise the telemonitoring scenario. The first solution was based on the HOTMES ontology-based architecture. The second solution was based on the most popular TCP/IP management architecture, SNMP (Simple Network Management Protocol). As a general conclusion, it has been demonstrated that the combination of ontologies, rules, WSs and the autonomic computing paradigm takes advantage of the main benefits that these technologies can offer in terms of knowledge representation, work flow organization, data transference, personalization of services and self-management capabilities. It has been proven that ontologies can be successfully used to provide clear descriptions of managed data (both clinical and technical) and ways of managing such information. This represents a further step towards the possibility of establishing more effective home-based telemonitoring systems and thus improving the remote care of patients with chronic diseases

Volume 13, issue 1

The mission of CJS is to contribute to the effective continuing medical education of Canadian surgical specialists, using innovative techniques when feasible, and to provide surgeons with an effective vehicle for the dissemination of observations in the areas of clinical and basic science research. Visit the journal website at http://canjsurg.ca/ for more.https://ir.lib.uwo.ca/cjs/1098/thumbnail.jp

Advancing probabilistic and causal deep learning in medical image analysis

The power and flexibility of deep learning have made it an indispensable tool for tackling modern machine learning problems. However, this flexibility comes at the cost of robustness and interpretability, which can lead to undesirable or even harmful outcomes. Deep learning models often fail to generalise to real-world conditions and produce unforeseen errors that hinder wide adoption in safety-critical critical domains such as healthcare. This thesis presents multiple works that address the reliability problems of deep learning in safety-critical domains by being aware of its vulnerabilities and incorporating more domain knowledge when designing and evaluating our algorithms. We start by showing how close collaboration with domain experts is necessary to achieve good results in a real-world clinical task - the multiclass semantic segmentation of traumatic brain injuries (TBI) lesions in head CT. We continue by proposing an algorithm that models spatially coherent aleatoric uncertainty in segmentation tasks by considering the dependencies between pixels. The lack of proper uncertainty quantification is a robustness issue which is ubiquitous in deep learning. Tackling this issue is of the utmost importance if we want to deploy these systems in the real world. Lastly, we present a general framework for evaluating image counterfactual inference models in the absence of ground-truth counterfactuals. Counterfactuals are extremely useful to reason about models and data and to probe models for explanations or mistakes. As a result, their evaluation is critical for improving the interpretability of deep learning models.Open Acces

Tuberculosis diagnosis from pulmonary chest x-ray using deep learning.

Doctoral Degree. University of KwaZulu-Natal, Durban.Tuberculosis (TB) remains a life-threatening disease, and it is one of the leading causes of mortality in developing countries. This is due to poverty and inadequate medical resources. While treatment for TB is possible, it requires an accurate diagnosis first. Several screening tools are available, and the most reliable is Chest X-Ray (CXR), but the radiological expertise for accurately interpreting the CXR images is often lacking. Over the years, CXR has been manually examined; this process results in delayed diagnosis, is time-consuming, expensive, and is prone to misdiagnosis, which could further spread the disease among individuals. Consequently, an algorithm could increase diagnosis efficiency, improve performance, reduce the cost of manual screening and ultimately result in early/timely diagnosis. Several algorithms have been implemented to diagnose TB automatically. However, these algorithms are characterized by low accuracy and sensitivity leading to misdiagnosis. In recent years, Convolutional Neural Networks (CNN), a class of Deep Learning, has demonstrated tremendous success in object detection and image classification task. Hence, this thesis proposed an efficient Computer-Aided Diagnosis (CAD) system with high accuracy and sensitivity for TB detection and classification. The proposed model is based firstly on novel end-to-end CNN architecture, then a pre-trained Deep CNN model that is fine-tuned and employed as a features extractor from CXR. Finally, Ensemble Learning was explored to develop an Ensemble model for TB classification. The Ensemble model achieved a new stateof- the-art diagnosis accuracy of 97.44% with a 99.18% sensitivity, 96.21% specificity and 0.96% AUC. These results are comparable with state-of-the-art techniques and outperform existing TB classification models.Author's Publications listed on page iii

Corynebacterium parvum treatment in normal and tumour bearing hosts

Corynebacterium Parvum is an anaerobic diphtherioid bacterium which suppresses the rate of development and spread of a wide variety of transplantable rodent tumours. It has also been suggested that the bacterium might be of therapeutic benefit to humans suffering from cancer. The experimental work of this thesis discusses some of the effects of C. Parvum on mononuclear phagocyte cells both in normal rats, and rats bearing carcinogen induced cancers of the colon. Particular attention has been paid to this class of cells since it is considered that they are the mediators of the anti tumour effects of the bacterium. In addition, the results of two clinical trials reporting the results of C. Parvum in the treatment of post operative human cancers are presented. Experimental Work The dose of C. Parvum used in the experimental work was calculated from preliminary experiments. C. Parvum was given to adult female Wistar Rats by a variety of routes at different dose rates. Stimulation of the reticuloendothelial system was assessed by measurements of the resulting splenomegaly. It was considered that the optimum dose calculated by this work would also cause stimulation of mononuclear phagocytes. An experimental rat model has been described at the Sir William Dunn School of Pathology in Oxford which enables the continuous collection of effluent gut lymph containing mononuclear phagocytes. It is argued that these cells represent tissue mononuclear phagocytes and lymph collected in this way enables a dynamic assessment of cell traffic in the gut wall. A dose of C. Parvum which caused marked reticuloendothelial stimulation had no measured effect on these gut associated mononuclear cells nor on cells showing the capacity to ingest antibody coated sheep red blood cells. These studies were repeated in rats bearing small colonic cancers induced by dimethylhydrazine. Once again the dose of C. Parvum which was given had no effect on gut associated mononuclear cells or phagocytes. However, the numbers of phagocytic cells were markedly suppressed in all of the tumour bearing rats, whether or not they had been treated with C. Parvum. Since the dose of C. Parvum caused splenomegaly but had no effects on mononuclear phagocytes in effluent gut lymph, other effects of this treatment were studied. The dose of C. Parvum which was used caused; 1. Blood changes : an immediate lymphocytopaenia with a concomittant granulocytosis in peripheral blood. Subsequently the granulocyte level returned to normal levels but the lymphocytes showed a small, but significant increase above the original value by two weeks after treatment. Monocyte numbers in the peripheral blood were not affected. 2. Peritoneal exudates and splenic macerates : esterase positive and phagocytic cells obtained from both of these sources showed a fall one week after treatment followed by a rise by two weeks. 3. Histological changes : there was a marked increase in the area of the white pulp, and probably also the red pulp of the spleen. Both the spleen and caecal lymph nodes showed histological evidence of stimulation of the reticuloendothelial system. It had been hoped to identify tissue macrophages using acid phosphatase, glucuronidase and non specific esterase staining techniques. However, it was not possible to assess post treatment changes by this method. Clinical Two clinical trials are reported describing the use of C. Parvum in the adjuvant treatment of two common human cancers. In the first of these, the bacterium was given in a small intradermal dose along with an inoculum of autologous irradiated tumour cells to post operative patients after resection of Stage 1 and 2 lung cancer. In this trial an attempt was made to stimulate specific immunity against residual tumour cells. In the second trial C. Parvum was given by serial intravenous injections to patients who had undergone resection of Dukes stage B & C colorectal cancers. This approach attempted to non specifically stimulate the reticuloendothelial system with the aim of increasing the destruction of residual tumour cells by "activated" mononuclear phagocytes. In each trial nearly 100 patients were included with an equal distribution between the treatment and the control groups. It was apparent that the median times to recurrence or death were not influenced by the treatment in either trial. Considerable enthusiasm for the use of bacteria in the treatment of human cancers was generated in the early 1970's. This followed reports of improved remission rates notably after the use of B.C.G. in the treatment of acute lymphoblastic leukaemia and malignant melanoma. Carefully constructed clinical trials have failed to support the continued use of this kind of treatment. Both the experimental and clinical work described in this thesis suggest that immune stimulation mediated by bacteria will not provide an easy answer to the treatment of human malignant disease