Using Convolutional Neural Networks with Multiple Thermal Sensors for Unobtrusive Pose Recognition.

The desire to remain living in one's own home rather than a care home by those in need of 24/7 care is one that requires a level of understanding for the actions of an environment's inhabitants. This can potentially be accomplished with the ability to recognise Activities of Daily Living (ADLs); however, this research focuses first on producing an unobtrusive solution for pose recognition where the preservation of privacy is a primary aim. With an accurate manner of predicting an inhabitant's poses, their interactions with objects within the environment and, therefore, the activities they are performing, can begin to be understood. This research implements a Convolutional Neural Network (CNN), which has been designed with an original architecture derived from the popular AlexNet, to predict poses from thermal imagery that have been captured using thermopile infrared sensors (TISs). Five TISs have been deployed within the smart kitchen in Ulster University where each provides input to a corresponding trained CNN. The approach is evaluated using an original dataset and an F1-score of 0.9920 was achieved with all five TISs. The limitations of utilising a ceiling-based TIS are investigated and each possible permutation of corner-based TISs is evaluated to satisfy a trade-off between the number of TISs, the total sensor cost and the performances. These tests are also promising as F1-scores of 0.9266, 0.9149 and 0.8468 were achieved with the isolated use of four, three, and two corner TISs, respectively.

Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study.

BACKGROUND: The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed. OBJECTIVE: This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects. METHODS: Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed. RESULTS: Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work. CONCLUSIONS: Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

Breast density classification in mammograms: An investigation of encoding techniques in binary-based local patterns.

We investigate various channel encoding techniques applied to breast density classification in mammograms; specifically, local binary, ternary, and quinary encoding approaches are considered. Subsequently, we propose a new encoding approach based on a seven-encoding technique, yielding a new local pattern operator called a local septenary pattern operator. Experimental results suggest that the proposed local pattern operator is robust and outperforms the other encoding techniques when evaluated on the Mammographic Image Analysis Society (MIAS) and InBreast datasets. The local septenary pattern operator achieved a maximum classification accuracy of 83.3% and 80.5% on the MIAS and InBreast datasets, respectively. The closest comparison achieved by the other local pattern operators is the local quinary operator, with maximum accuracies of 82.1% (MIAS) and 80.1% (InBreast), respectively.

Plasma glial fibrillary acidic protein and neurofilament light chain, but not tau, are biomarkers of sports-related mild traumatic brain injury.

Mild traumatic brain injury is a relatively common event in contact sports and there is increasing interest in the long-term neurocognitive effects. The diagnosis largely relies on symptom reporting and there is a need for objective tools to aid diagnosis and prognosis. There are recent reports that blood biomarkers could potentially help triage patients with suspected injury and normal CT findings. We have measured plasma concentrations of glial and neuronal proteins and explored their potential in the assessment of mild traumatic brain injury in contact sport. We recruited a prospective cohort of active male rugby players, who had pre-season baseline plasma sampling. From this prospective cohort, we recruited 25 players diagnosed with mild traumatic brain injury. We sampled post-match rugby players without head injuries as post-match controls. We measured plasma neurofilament light chain, tau and glial fibrillary acidic protein levels using ultrasensitive single molecule array technology. The data were analysed at the group and individual player level. Plasma glial fibrillary acidic protein concentration was significantly increased 1-h post-injury in mild traumatic brain injury cases compared to the non-injured group (P = 0.017). Pairwise comparison also showed that glial fibrillary acidic protein levels were higher in players after a head injury in comparison to their pre-season levels at both 1-h and 3- to 10-day post-injury time points (P = 0.039 and 0.040, respectively). There was also an increase in neurofilament light chain concentration in brain injury cases compared to the pre-season levels within the same individual at both time points (P = 0.023 and 0.002, respectively). Tau was elevated in both the non-injured control group and the 1-h post-injury group compared to pre-season levels (P = 0.007 and 0.015, respectively). Furthermore, receiver operating characteristic analysis showed that glial fibrillary acidic protein and neurofilament light chain can separate head injury cases from control players. The highest diagnostic power was detected when biomarkers were combined in differentiating 1-h post-match control players from 1-h post-head injury players (area under curve 0.90, 95% confidence interval 0.79-1.00, P < 0.0002). The brain astrocytic marker glial fibrillary acidic protein is elevated in blood 1 h after mild traumatic brain injury and in combination with neurofilament light chain displayed the potential as a reliable biomarker for brain injury evaluation. Plasma total tau is elevated following competitive rugby with and without a head injury, perhaps related to peripheral nerve trauma and therefore total tau does not appear to be suitable as a blood biomarker.

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network.

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find 'contour-like' objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial pectoral muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual pectoral muscle boundary. Finally, we developed two different post-processing steps to find the actual pectoral muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ±â€¯8.5% and 97.5 ±â€¯6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases.

Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours.

In medical computer aided diagnosis systems, image segmentation is one of the major pre-processing steps used to ensure only the region of interest, such as the breast region, will be processed in subsequent steps. Nevertheless, breast segmentation is a difficult task due to low contrast and inhomogeneity, especially when estimating the chest wall in magnetic resonance (MR) images. In fact, the chest wall comprises fat, skin, muscles, and the thoracic skeleton, which can misguide automatic methods when attempting to estimate its location. The objective of the study is to develop a fully automated method for breast and pectoral muscle boundary estimation in MR images. Firstly, we develop a 2D breast mathematical model based on 30 MRI slices (from a patient) and identify important landmarks to obtain a model for the general shape of the breast in an axial plane. Subsequently, we use Otsu's thresholding approach and Canny edge detection to estimate the breast boundary. The active contour method is then employed using both inflation and deflation forces to estimate the pectoral muscle boundary by taking account of information obtained from the proposed 2D model. Finally, the estimated boundary is smoothed using a median filter to remove outliers. Our two datasets contain 60 patients in total and the proposed method is evaluated based on 59 patients (one patient is used to develop the 2D breast model). On the first dataset (9 patients) the proposed method achieved Jaccard = 81.1% ±6.1 % and dice coefficient= 89.4% ±4.1 % and on the second dataset (50 patients) Jaccard = 84.9% ±5.8 % and dice coefficient = 92.3% ±3.6 %. These results are qualitatively comparable with the existing methods in the literature.

Fully automated breast boundary and pectoral muscle segmentation in mammograms.

Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to search for the actual boundary. A post-processing technique is proposed to correct the overestimated boundary caused by artifacts. The pectoral muscle boundary is estimated using Canny edge detection and a pre-processing technique is proposed to remove noisy edges. Subsequently, we identify five edge features to find the edge that has the highest probability of being the initial pectoral contour and search for the actual boundary via contour growing. The segmentation results for the proposed method are compared with manual segmentations using 322, 208 and 100mammograms from the Mammographic Image Analysis Society (MIAS), INBreast and Breast Cancer Digital Repository (BCDR) databases, respectively. Experimental results show that the breast boundary and pectoral muscle estimation methods achieved dice similarity coefficients of 98.8% and 97.8% (MIAS), 98.9% and 89.6% (INBreast) and 99.2% and 91.9% (BCDR), respectively.

A Geospatial Semantic Enrichment and Query Service for Geotagged Photographs.

With the increasing abundance of technologies and smart devices, equipped with a multitude of sensors for sensing the environment around them, information creation and consumption has now become effortless. This, in particular, is the case for photographs with vast amounts being created and shared every day. For example, at the time of this writing, Instagram users upload 70 million photographs a day. Nevertheless, it still remains a challenge to discover the "right" information for the appropriate purpose. This paper describes an approach to create semantic geospatial metadata for photographs, which can facilitate photograph search and discovery. To achieve this we have developed and implemented a semantic geospatial data model by which a photograph can be enrich with geospatial metadata extracted from several geospatial data sources based on the raw low-level geo-metadata from a smartphone photograph. We present the details of our method and implementation for searching and querying the semantic geospatial metadata repository to enable a user or third party system to find the information they are looking for.

A sensor and video based ontology for activity recognition in smart environments.

Activity recognition is used in a wide range of applications including healthcare and security. In a smart environment activity recognition can be used to monitor and support the activities of a user. There have been a range of methods used in activity recognition including sensor-based approaches, vision-based approaches and ontological approaches. This paper presents a novel approach to activity recognition in a smart home environment which combines sensor and video data through an ontological framework. The ontology describes the relationships and interactions between activities, the user, objects, sensors and video data.

Dynamic iris biometry: a technique for enhanced identification.

BACKGROUND: The iris as a unique identifier is predicated on the assumption that the iris image does not alter. This does not consider the fact that the iris changes in response to certain external factors including medication, disease, surgery as well as longer term ageing changes. It is also part of a dynamic optical system that alters with light level and focussing distance. A means of distinguishing the features that do not alter over time from those that do is needed. This paper applies iris recognition algorithms to a newly acquired database of 186 iris images from four subjects. These images have greater magnification and detail than iris images in existing databases. Iris segmentation methods are tested on the database. A new technique that enhances segmentation is presented and compared to two existing methods. These are also applied to test the effects of pupil dilation in the identification process. FINDINGS: Segmentation results from all the images showed that using the proposed algorithm accurately detected pupil boundaries for 96.2% respectively of the images, which was an increase of 88.7% over the most commonly used algorithm. For the images collected, the proposed technique also showed significant improvement in detection of the limbal boundary compared to the detection rates using existing methods. With regard to boundary displacement errors, only slight errors were found with the proposed technique compared to extreme errors made when existing techniques were applied. As the pupil becomes more dilated, the success of identification is increasingly more dependent on the decision criterion used. CONCLUSIONS: The enhanced segmentation technique described in this paper performs with greater accuracy than existing methods for the higher quality images collected in this study. Implementation of the proposed segmentation enhancement significantly improves pupil boundary detection and therefore overall iris segmentation. Pupil dilation is an important aspect of iris identification; with increasing dilation, there is a greater risk of identification failure. Choice of decision criterion for identification should be carefully reviewed. It needs to be recognised that differences in the quality of images in different databases may result in variations in the performance of iris recognition algorithms.

Phase I trial of large multivalent immunogen derived from melanoma lysates in patients with disseminated melanoma.

PURPOSE: The purpose of this research was to determine the toxicity and immunological activity of large multivalent immunogen (LMI), a preparation of tumor cell membranes affixed to amorphous silica microbeads, in patients with melanoma. EXPERIMENTAL DESIGN: Nineteen patients with metastatic (stage IV) melanoma were entered into the study, of whom 15 received the full 3 months of treatment with LMI. LMI was administered without adjuvant, one-half intradermally (i.d.) and the other half s.c. Because we expected little toxicity, we first treated 2 patients at each dose level, 10-, 30-, or 100-million tumor cell equivalents on weeks 0, 4, and 8, and subsequently randomized the remaining 13 patients to receive treatment with one of those dosage schedules, for a total of 19 patients. Two patients who were registered were found to be ineligible because of brain metastases, and 2 others did not complete the course of treatment for reasons other than toxicity. Thus, 15 patients were fully evaluable. Patients with evidence of a clinical response (at least stable disease at the 12-week checkpoint) had the option of continuing treatment at 4-week intervals. Frequencies of cytolytic T cell precursors against HLA-A2 matched melanoma cells, and delayed-type hypersensitivity to a melanoma cell membrane preparation from a component melanoma cell line were performed to measure immunological efficacy, and serum chemistries and complete blood counts were performed every 2 weeks throughout the study to measure possible toxicity. Computed tomography scans were performed pretreatment and at week 12 to measure possible beneficial effects on known lesions. RESULTS: Eight of the 15 evaluable patients had an increase in cytolytic T-cell precursors during the course of therapy, usually by day 42. No patient had demonstrable delayed-type hypersensitivity to a melanoma membrane preparation before or after treatment. No toxicity of any kind was observed. A degree of clinical effectiveness of LMI was suggested by the elicitation of stable disease in 5 patients at 12 weeks. One patient had >50% regression of a lung nodule but progression of disease to the brain, whereas a second patient had a bona fide partial remission of a 3-cm diameter solitary lung nodule. CONCLUSIONS: LMI was nontoxic, improved immunological reactivity to melanoma cells, and showed evidence of clinical effectiveness (shrinkage of tumor) in 1 patient. Additional studies with LMI with added adjuvant materials, in melanoma and other cancers, appear warranted.