Characterization of Adipose Tissue Inflammation in Alcoholic Liver Disease

Adipose tissue inflammation has an impact on liver health and it has been demonstrated that chronic alcohol consumption leads to the expression of pro-inflammatory markers in the adipose tissue. A thorough characterization of alcohol-induced adipose inflammation is lacking, and is important to understand in order to identify immune-related mechanisms that drive this phenomenon. Current therapeutic regimens for alcoholic liver disease are ineffective. It is critical to understand how other organs influence liver injury in this disease when developing novel and effective therapies in the future. Alcoholic liver disease exhibits a sexual dimorphism; women are more susceptible to liver injury than men and the same paradigm exists in rodent models. Here, I demonstrate that female mice have greater alcohol-induced adipose tissue inflammation than male mice, evidenced by greater expression of pro-inflammatory cytokines and cell markers. Further, female mice also exhibit higher expression of toll-like receptor genes in the adipose tissue, suggesting a potential role for the innate immune system in alcohol-induced adipose inflammation. Toll-like receptor 4 (TLR4) has been demonstrated to drive inflammation in both the liver and adipose tissue. I used both germline and conditional knockouts of Tlr4 to characterize alcohol-induced changes in the immune cell composition of adipose tissue. Alcohol increased the number of pro-inflammatory adipose tissue macrophages. This macrophage phenotype switching is partially dependent on TLR4; germline, but not myeloid-specific, Tlr4-deletion prevents macrophage phenotype switching. Overall, my work demonstrates that alcohol-induced adipose tissue inflammation is related to liver injury and that TLR4 contributes to adipose macrophage phenotype switching

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

Review of Positron Emission Tomography at Royal Prince Alfred Hospital, CHERE Project Report No 18

This report is a review of the clinical uses, impacts on clinical management, clinical outcome and resource use of Positron Emission Tomography (PET) at Royal Prince Alfred Hospital (RPAH).Positron emission tomography

Simultaneous estimation of physiological parameters and the input function : in vivo PET data

Author name used in this publication: (David) Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Estimation of input function and kinetic parameters using simulated annealing : application in a flow model

Author name used in this publication: Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Improving Skin Lesion Segmentation via Stacked Adversarial Learning

Segmentation of skin lesions is an essential step in computer aided diagnosis (CAD) for the automated melanoma diagnosis. Recently, segmentation methods based on fully convolutional networks (FCNs) have achieved great success for general images. This success is primarily related to FCNs leveraging large labelled datasets to learn features that correspond to the shallow appearance and the deep semantics of the images. Such large labelled datasets, however, are usually not available for medical images. So researchers have used specific cost functions and post-processing algorithms to refine the coarse boundaries of the results to improve the FCN performance in skin lesion segmentation. These methods are heavily reliant on tuning many parameters and post-processing techniques. In this paper, we adopt the generative adversarial networks (GANs) given their inherent ability to produce consistent and realistic image features by using deep neural networks and adversarial learning concepts. We build upon the GAN with a novel stacked adversarial learning architecture such that skin lesion features can be learned, iteratively, in a class-specific manner. The outputs from our method are then added to the existing FCN training data, thus increasing the overall feature diversity. We evaluated our method on the ISIC 2017 skin lesion segmentation challenge dataset; we show that it is more accurate and robust when compared to the existing skin state-of-the-art methods