Deep learning and localized features fusion for medical image classification

Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible. This dissertation tries to tackle these problems, first by developing a localized feature-based classification system for medical images and using these features and to give a classification for the entire image, and second, by improving the computational complexity of feature analysis to make it viable as a diagnostic aid system in practical clinical situations. For local feature development, a new approach based on combining the rising deep learning paradigm with the use of handcrafted features is developed to classify cervical tissue histology images into different cervical intra-epithelial neoplasia classes. Using deep learning combined with handcrafted features improved the accuracy by 8.4% achieving 80.72% exact class classification accuracy compared to 72.29% when using the benchmark feature-based classification method --Abstract, page iv

UWOMJ Volume 68, Number 1, Winter 1999

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1245/thumbnail.jp

Case series of breast fillers and how things may go wrong: radiology point of view

INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging breast due to breastfeeding or aging as well as small breast size. Recent years have shown the emergence of a variety of injectable materials on market as breast fillers. These injectable breast fillers have swiftly gained popularity among women, considering the minimal invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know that the procedure may pose detrimental complications, while visualization of breast parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic challenges. We present a case series of three patients with prior history of hyaluronic acid and collagen breast injections. REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening shortness of breath, non-productive cough, central chest pain; associated with fever and chills for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases revealed non thrombotic wedge-shaped peripheral air-space densities. The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2- weeks duration. Previous collagen breast injection performed 1 year ago had impeded sonographic visualization of the breast parenchyma. MRI breasts showed multiple non- enhancing round and oval shaped lesions exhibiting fat intensity. CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well as limitations of imaging posed by breast fillers such that MRI is required as problem-solving tool

Characterization of alar ligament on 3.0T MRI: a cross-sectional study in IIUM Medical Centre, Kuantan

INTRODUCTION: The main purpose of the study is to compare the normal anatomy of alar ligament on MRI between male and female. The specific objectives are to assess the prevalence of alar ligament visualized on MRI, to describe its characteristics in term of its course, shape and signal homogeneity and to find differences in alar ligament signal intensity between male and female. This study also aims to determine the association between the heights of respondents with alar ligament signal intensity and dimensions. MATERIALS & METHODS: 50 healthy volunteers were studied on 3.0T MR scanner Siemens Magnetom Spectra using 2-mm proton density, T2 and fat-suppression sequences. Alar ligament is depicted in 3 planes and the visualization and variability of the ligament courses, shapes and signal intensity characteristics were determined. The alar ligament dimensions were also measured. RESULTS: Alar ligament was best depicted in coronal plane, followed by sagittal and axial planes. The orientations were laterally ascending in most of the subjects (60%), predominantly oval in shaped (54%) and 67% showed inhomogenous signal. No significant difference of alar ligament signal intensity between male and female respondents. No significant association was found between the heights of the respondents with alar ligament signal intensity and dimensions. CONCLUSION: Employing a 3.0T MR scanner, the alar ligament is best portrayed on coronal plane, followed by sagittal and axial planes. However, tremendous variability of alar ligament as depicted in our data shows that caution needs to be exercised when evaluating alar ligament, especially during circumstances of injury

Implementation of the JAK2V617F mutation analysis in the pathway of suspected myeloproliferative neoplasms in Groote Schuur Hospital

We studied the implementation of JAK2 mutation analysis in conjunction with the World Health Organisation (WHO) guidelines in the pathway to MPN diagnosis in 279 patients presenting with one of three clinical scenarios: erythrocytosis, OR leukocytosis and/or thrombocytosis and/or splenomegaly; OR patients with thrombosis without cytoses. Patients were investigated for MPN and managed in the haematology clinic of Groote Schuur Hospital. We studied the association of clinical and laboratory variables with clonal vs non-clonal diagnoses. In 120/297 patients MPN was confirmed: Polycythemia vera (PV), (n=51, 100% JAK2 mutated); essential thrombocytosis, (n=41, 42% JAK2 mutated); primary myelofibrosis (n=28, 57% JAK2 mutated). The 2016 WHO haemoglobin/haematocrit thresholds in PV were validated. Idiopathic erythrocytosis (IE) found in 44 patients. Bone marrow histology, but not serum EPO level, was essential to differentiate between clonal and non-clonal erythrocytosis. Both PV and IE patients complied with the criteria of absolute erythrocytosis on peripheral blood, yet nuclear red cell mass identified critical differences between clonal and non-clonal erythrocytosis. No patient venesected for nonclonal erythropoiesis developed thrombocytosis. JAK2 mutation analysis applied with the WHO diagnostic algorithm efficiently differentiated true clonal myeloproliferation from reactive cytoses. Lifestyle and metabolic factors such as smoking and thrombosis were not associated with either clonal or non-clonal erythrocytosis, and were equally present in mutated and unmutated essential thrombocytosis

The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis

Information Technology and Childbirth Education

Information Technology and Childbirth Educatio

Speaking Their Language: Integrating Social Media into Childbirth Education Practice

With the advancement of modern technology, the internet has become a standard platform for many forms of communication and education. The majority of pregnant females fall into the cohort known as Millenials and have experienced technology since early in life. Millenials consider technology as part of their everyday life and use it for personal interaction or a source of information. The established comfort with the use of technology combined with busy lifestyles, multiple commitments, transportation costs or logistics, childcare, or a desire for privacy, support the use of perinatal online learning. This article examines options that childbirth educators may consider for integrating social media or other forms of technology into their repertoire

Pregnancy Apps: A Closer Look at the Implications for Childbirth Educators

Most pregnant women download an average of three pregnancy apps during their gestational period. There are no set standards in place for what needs to be included in an app’s description leaving consumers to decide for themselves when it comes to selecting the right app to download. The childbirth educator must be knowledgeable about pregnancy apps, in-tune as to what apps their clientele download, and how to analyze them for credibility. This article presents characteristics associated with women of childbearing age (i.e. Millennial/Net generation); the reasons why pregnant women are turning to apps; the limitations of apps, and the childbirth educator’s role in a smartphone app culture