Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Hetero-Modal Variational Encoder-Decoder for Joint Modality Completion and Segmentation

We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities. Instead of producing one network for each possible subset of observed modalities or using arithmetic operations to combine feature maps, our hetero-modal variational 3D encoder-decoder independently embeds all observed modalities into a shared latent representation. Missing data and tumour segmentation can be then generated from this embedding. In our scenario, the input is a random subset of modalities. We demonstrate that the optimisation problem can be seen as a mixture sampling. In addition to this, we introduce a new network architecture building upon both the 3D U-Net and the Multi-Modal Variational Auto-Encoder (MVAE). Finally, we evaluate our method on BraTS2018 using subsets of the imaging modalities as input. Our model outperforms the current state-of-the-art method for dealing with missing modalities and achieves similar performance to the subset-specific equivalent networks.Comment: Accepted at MICCAI 201

Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans

Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the output of the SE block is fed directly into subsequent bottleneck layers to reduce the number of channels. Finally, the calibrated 2D slices are concatenated to obtain multimodal features through a 2D convolutional neural network (CNN) for prediction of the central pixel. In our architecture, both local inter-slice and global intra-slice features are jointly exploited to predict class label of the central voxel in a given patch through the 2D CNN classifier. We implicitly apply all modalities through trainable parameters to assign weights to the contributions of each sequence for segmentation. Experimental results on the segmentation of brain tumors in multimodal MRI scans (BraTS'19) demonstrate that our proposed method can efficiently segment the tumor regions

M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Although many OS time prediction methods have been developed and obtain promising results, there are still several issues. First, conventional prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume, which may not represent the full image or model complex tumor patterns. Second, different types of scanners (i.e., multi-modal data) are sensitive to different brain regions, which makes it challenging to effectively exploit the complementary information across multiple modalities and also preserve the modality-specific properties. Third, existing methods focus on prediction models, ignoring complex data-to-label relationships. To address the above issues, we propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net). Specifically, we first project the 3D MR volume onto 2D images in different directions, which reduces computational costs, while preserving important information and enabling pre-trained models to be transferred from other tasks. Then, we use a modality-specific network to extract implicit and high-level features from different MR scans. A multi-modal shared network is built to fuse these features using a bilinear pooling model, exploiting their correlations to provide complementary information. Finally, we integrate the outputs from each modality-specific network and the multi-modal shared network to generate the final prediction result. Experimental results demonstrate the superiority of our M2Net model over other methods.Comment: Accepted by MICCAI'2

Primary antibody deficiency in a tertiary referral hospital: A 30-year experiment

Background: Primary antibody deficiency (PAD) is the most common group of primary immunodeficiency disorders (PID), with a broad spectrum of clinical features ranging from severe and recurrent infections to asymptomatic disease. Objectives: The current study was performed to evaluate and compare demographic and clinical data in the most common types of PAD. Materials and Methods: We performed a retrospective review of the medical records of all PAD patients with a confirmed diagnosis of common variable immunodeficiency (CVID), hyper IgM syndrome (HIgM), selective IgA deficiency (SIgAD), and X-linked agammaglobulinemia (XLA) who were diagnosed during the last 30 years at the Children�s Medical Center, Tehran, Iran. Results: A total number of 280 cases of PAD (125 CVID, 32 HIgM, 63 SIgAD, and 60 XLA) were enrolled in the study. The median (range) age at the onset of disease in CVID, HIgM, SIgAD, and XLA was 2 (0-46), 0.91 (0-9), 1 (0-26), and 1 (0-10) years, respectively. Gastrointestinal infections were more prevalent in CVID patients, as were central nervous system infections in XLA patients. Autoimmune complications were more prevalent in HIgM patients, malignancies in CVID patients, and allergies in SIgAD patients. The mortality rate for CVID, HIgM, and XLA was 27.2, 28.1, and 25, respectively. No deaths were reported in SIgAD patients. Conclusions: SIgAD patients had the best prognosis. While all PAD patients should be monitored for infectious complications, special attention should be paid to the finding of malignancy and autoimmune disorders in CVID and HIgM patients, respectively. © 2015 Esmon Publicidad

Association of Panton Valentine Leukocidin (PVL) genes with methicillin resistant Staphylococcus aureus (MRSA) in Western Nepal: a matter of concern for community infections (a hospital based prospective study)

BACKGROUND: Methicillin resistant Staphylococcus aureus (MRSA) is a major human pathogen associated with nosocomial and community infections. Panton Valentine leukocidin (PVL) is considered one of the important virulence factors of S. aureus responsible for destruction of white blood cells, necrosis and apoptosis and as a marker of community acquired MRSA. This study was aimed to determine the prevalence of PVL genes among MRSA isolates and to check the reliability of PVL as marker of community acquired MRSA isolates from Western Nepal. METHODS: A total of 400 strains of S. aureus were collected from clinical specimens and various units (Operation Theater, Intensive Care Units) of the hospital and 139 of these had been confirmed as MRSA by previous study. Multiplex PCR was used to detect mecA and PVL genes. Clinical data as well as antimicrobial susceptibility data was analyzed and compared among PVL positive and negative MRSA isolates. RESULTS: Out of 139 MRSA isolates, 79 (56.8 %) were PVL positive. The majority of the community acquired MRSA (90.4 %) were PVL positive (Positive predictive value: 94.9 % and negative predictive value: 86.6 %), while PVL was detected only in 4 (7.1 %) hospital associated MRSA strains. None of the MRSA isolates from hospital environment was found positive for the PVL genes. The majority of the PVL positive strains (75.5 %) were isolated from pus samples. Antibiotic resistance among PVL negative MRSA isolates was found higher as compared to PVL positive MRSA. CONCLUSION: Our study showed high prevalence of PVL among community acquired MRSA isolates. Absence of PVL among MRSA isolates from hospital environment indicates its poor association with hospital acquired MRSA and therefore, PVL may be used a marker for community acquired MRSA. This is first study from Nepal, to test PVL among MRSA isolates from hospital environment. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-016-1531-1) contains supplementary material, which is available to authorized users

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

The Nursing Leadership Institute program evaluation: a critique

Farinaz Havaei, Maura MacPhee School of Nursing, University of British Columbia, Vancouver, BC, Canada Abstract: A theory-driven program evaluation was conducted for a nursing leadership program, as a collaborative project between university faculty, the nurses' union, the provincial Ministry of Health, and its chief nursing officers. A collaborative logic model process was used to engage stakeholders, and mixed methods approaches were used to answer evaluation questions. Despite demonstrated, successful outcomes, the leadership program was not supported with continued funding. This paper examines what happened during the evaluation process: What factors failed to sustain this program? Keywords: leadership development, theory-driven evaluation, mixed methods, collaborative logic modelin

Validity and reliability of the Persian version of the PRAFAB questionnaire in Iranian women with urinary incontinence

Introduction and hypothesisUrinary incontinence (UI) is a common disorder in women that can affect a person's quality of life. There are several instruments to assess the severity of urinary incontinence. One of the common tools is the Protection, Amount, Frequency, Adjustment, Body image (PRAFAB) questionnaire. Therefore, this study was performed with the aim of assessing the validity and reliability of the Persian version of the PRAFAB questionnaire.MethodsFirst, the English version of the questionnaire was translated into Persian. Second, the psychometric properties of the Persian version were collected in 60 women with urinary incontinence referred to Al-Zahra Hospital by an expert team. Content validity (CV) was evaluated through CV index (CVI) and CV ratio (CVR). Construct validity was evaluated using exploratory factor analysis and reproducibility was tested based on test-retest reliability using intraclass correlation coefficient (ICC). Internal consistency was calculated using Cronbach's alpha.ResultsThe results showed acceptable CVI in relevancy, clarity, and simplicity, acceptable CVR for all items, good internal consistency (Cronbach's alpha = 0.738) and excellent repeatability (ICC = 0.98).ConclusionThe Persian version of the PRAFAB questionnaire has acceptable validity and reliability and in future it can be used as a suitable evaluation instrument to assess urinary incontinence in Iranian women

Supervised two-stage transfer learning on imbalanced dataset for sport classification

\u3cp\u3eSport classification is a crucial step for content analysis in a sport stream monitoring system. Training a reliable sport classifier can be a challenging task when the data is limited in amount and highly imbalanced. In this paper, we introduce a supervised two-stage transfer learning (Two-Stage-TL) method to solve the data shortage problem. It can progressively transfer features from a source domain to the target domain using a properly selected bridge domain. For the class imbalance issue, we compare several existing methods and demonstrate that the log-smoothing class weight is the most applicable way for this specific problem. Extensive experiments are conducted using ResNet50, VGG16, and Inception-ResNet-v2. The results show that Two-Stage-TL outperforms classical One-Stage-TL and achieves the best performance using log-smoothing class weight. The in-depth analysis is useful for researchers and developers in solving similar problems.\u3c/p\u3