Brain tumor segmentation with missing modalities via latent multi-source correlation representation

Multimodal MR images can provide complementary information for accurate brain tumor segmentation. However, it's common to have missing imaging modalities in clinical practice. Since there exists a strong correlation between multi modalities, a novel correlation representation block is proposed to specially discover the latent multi-source correlation. Thanks to the obtained correlation representation, the segmentation becomes more robust in the case of missing modalities. The model parameter estimation module first maps the individual representation produced by each encoder to obtain independent parameters, then, under these parameters, the correlation expression module transforms all the individual representations to form a latent multi-source correlation representation. Finally, the correlation representations across modalities are fused via the attention mechanism into a shared representation to emphasize the most important features for segmentation. We evaluate our model on BraTS 2018 datasets, it outperforms the current state-of-the-art method and produces robust results when one or more modalities are missing.Comment: 9 pages, 6 figures, accepted by MICCAI 202

Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection with PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, there is not an accurate automated segmentation method. Segmentation tends to be done manually by different imaging experts and it is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT modalities, with the underlying assumption that each modality contains complementary information. However, these methods do not fully exploit the high PET tumor sensitivity that can guide the segmentation. We introduce a multimodal spatial attention module (MSAM) that automatically learns to emphasize regions (spatial areas) related to tumors and suppress normal regions with physiologic high-uptake. The resulting spatial attention maps are subsequently employed to target a convolutional neural network (CNN) for segmentation of areas with higher tumor likelihood. Our MSAM can be applied to common backbone architectures and trained end-to-end. Our experimental results on two clinical PET-CT datasets of non-small cell lung cancer (NSCLC) and soft tissue sarcoma (STS) validate the effectiveness of the MSAM in these different cancer types. We show that our MSAM, with a conventional U-Net backbone, surpasses the state-of-the-art lung tumor segmentation approach by a margin of 7.6% in Dice similarity coefficient (DSC)

Advanced maximum entropy approaches for medical and microscopy imaging

The maximum entropy framework is a cornerstone of statistical inference, which is employed at a growing rate for constructing models capable of describing and predicting biological systems, particularly complex ones, from empirical datasets.‎ In these high-yield applications, determining exact probability distribution functions with only minimal information about data characteristics and without utilizing human subjectivity is of particular interest. In this thesis, an automated procedure of this kind for univariate and bivariate data is employed to reach this objective through combining the maximum entropy method with an appropriate optimization method. The only necessary characteristics of random variables are their continuousness and ability to be approximated as independent and identically distributed. In this work, we try to concisely present two numerical probabilistic algorithms and apply them to estimate the univariate and bivariate models of the available data. In the first case, a combination of the maximum entropy method, Newton's method, and the Bayesian maximum a posteriori approach leads to the estimation of the kinetic parameters with arterial input functions (AIFs) in cases without any measurement of the AIF. ‎The results shows that the AIF can reliably be determined from the data of dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) by maximum entropy method. Then, kinetic parameters can be obtained. By using the developed method, a good data fitting and thus a more accurate prediction of the kinetic parameters are achieved, which, in turn, leads to a more reliable application of DCE-MRI. ‎ In the bivariate case, we consider colocalization as a quantitative analysis in fluorescence microscopy imaging. The method proposed in this case is obtained by combining the Maximum Entropy Method (MEM) and a Gaussian Copula, which we call the Maximum Entropy Copula (MEC). This novel method is capable of measuring the spatial and nonlinear correlation of signals to obtain the colocalization of markers in fluorescence microscopy images. Based on the results, MEC is able to specify co- and anti-colocalization even in high-background situations.‎ ‎The main point here is that determining the joint distribution via its marginals is an important inverse problem which has one possible unique solution in case of choosing an proper copula according to Sklar's theorem. This developed combination of Gaussian copula and the univariate maximum entropy marginal distribution enables the determination of a unique bivariate distribution. Therefore, a colocalization parameter can be obtained via Kendall’s t, which is commonly employed in the copula literature. In general, the importance of applying these algorithms to biological data is attributed to the higher accuracy, faster computing rate, and lower cost of solutions in comparison to those of others. The extensive application and success of these algorithms in various contexts depend on their conceptual plainness and mathematical validity. ‎ Afterward, a probability density is estimated via enhancing trial cumulative distribution functions iteratively, in which more appropriate estimations are quantified using a scoring function that recognizes irregular fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian criterion. Uncertainty induced by statistical fluctuations in random samples is reflected by multiple estimates for the probability density. In addition, as a useful diagnostic for visualizing the quality of the estimated probability densities, scaled quantile residual plots are introduced. Kullback--Leibler divergence is an appropriate measure to indicate the convergence of estimations for the probability density function (PDF) to the actual PDF as sample. The findings indicate the general applicability of this method to high-yield statistical inference.Die Methode der maximalen Entropie ist ein wichtiger Bestandteil der statistischen Inferenz, die in immer stärkerem Maße für die Konstruktion von Modellen verwendet wird, die biologische Systeme, insbesondere komplexe Systeme, aus empirischen Datensätzen beschreiben und vorhersagen können. In diesen ertragreichen Anwendungen ist es von besonderem Interesse, exakte Verteilungsfunktionen mit minimaler Information über die Eigenschaften der Daten und ohne Ausnutzung menschlicher Subjektivität zu bestimmen. In dieser Arbeit wird durch eine Kombination der Maximum-Entropie-Methode mit geeigneten Optimierungsverfahren ein automatisiertes Verfahren verwendet, um dieses Ziel für univariate und bivariate Daten zu erreichen. Notwendige Eigenschaften von Zufallsvariablen sind lediglich ihre Stetigkeit und ihre Approximierbarkeit als unabhängige und identisch verteilte Variablen. In dieser Arbeit versuchen wir, zwei numerische probabilistische Algorithmen präzise zu präsentieren und sie zur Schätzung der univariaten und bivariaten Modelle der zur Verfügung stehenden Daten anzuwenden. Zunächst wird mit einer Kombination aus der Maximum-Entropie Methode, der Newton-Methode und dem Bayes'schen Maximum-A-Posteriori-Ansatz die Schätzung der kinetischen Parameter mit arteriellen Eingangsfunktionen (AIFs) in Fällen ohne Messung der AIF ermöglicht. Die Ergebnisse zeigen, dass die AIF aus den Daten der dynamischen kontrastverstärkten Magnetresonanztomographie (DCE-MRT) mit der Maximum-Entropie-Methode zuverlässig bestimmt werden kann. Anschließend können die kinetischen Parameter gewonnen werden. Durch die Anwendung der entwickelten Methode wird eine gute Datenanpassung und damit eine genauere Vorhersage der kinetischen Parameter erreicht, was wiederum zu einer zuverlässigeren Anwendung der DCE-MRT führt. Im bivariaten Fall betrachten wir die Kolokalisierung zur quantitativen Analyse in der Fluoreszenzmikroskopie-Bildgebung. Die in diesem Fall vorgeschlagene Methode ergibt sich aus der Kombination der Maximum-Entropie-Methode (MEM) und einer Gaußschen Copula, die wir Maximum-Entropie-Copula (MEC) nennen. Mit dieser neuartigen Methode kann die räumliche und nichtlineare Korrelation von Signalen gemessen werden, um die Kolokalisierung von Markern in Bildern der Fluoreszenzmikroskopie zu erhalten. Das Ergebnis zeigt, dass MEC in der Lage ist, die Ko- und Antikolokalisation auch in Situationen mit hohem Grundrauschen zu bestimmen. Der wesentliche Punkt hierbei ist, dass die Bestimmung der gemeinsamen Verteilung über ihre Marginale ein entscheidendes inverses Problem ist, das eine mögliche eindeutige Lösung im Falle der Wahl einer geeigneten Copula gemäß dem Satz von Sklar hat. Diese neu entwickelte Kombination aus Gaußscher Kopula und der univariaten Maximum Entropie Randverteilung ermöglicht die Bestimmung einer eindeutigen bivariaten Verteilung. Daher kann ein Kolokalisationsparameter über Kendall's t ermittelt werden, der üblicherweise in der Copula-Literatur verwendet wird. Die Bedeutung der Anwendung dieser Algorithmen auf biologische Daten lässt sich im Allgemeinen mit hoher Genauigkeit, schnellerer Rechengesch windigkeit und geringeren Kosten im Vergleich zu anderen Lösungen begründen. Die umfassende Anwendung und der Erfolg dieser Algorithmen in verschiedenen Kontexten hängen von ihrer konzeptionellen Eindeutigkeit und mathematischen Gültigkeit ab. Anschließend wird eine Wahrscheinlichkeitsdichte durch iterative Erweiterung von kumulativen Verteilungsfunktionen geschätzt, wobei die geeignetsten Schätzungen mit einer Scoring-Funktion quantifiziert werden, um unregelmäßige Schwankungen zu erkennen. Dieses Kriterium verhindert eine Unter- oder Überanpassung der Daten als Alternative zur Verwendung des Bayes-Kriteriums. Die durch statistische Schwankungen in Stichproben induzierte Unsicherheit wird durch mehrfache Schätzungen für die Wahrscheinlichkeitsdichte berücksichtigt. Zusätzlich werden als nützliche Diagnostik zur Visualisierung der Qualität der geschätzten Wahrscheinlichkeitsdichten skalierte Quantil-Residuen-Diagramme eingeführt. Die Kullback-Leibler-Divergenz ist ein geeignetes Maß, um die Konvergenz der Schätzungen für die Wahrscheinlichkeitsdichtefunktion (PDF) zu der tatsächlichen PDF als Stichprobe anzuzeigen. Die Ergebnisse zeigen die generelle Anwendbarkeit dieser Methode für statistische Inferenz mit hohem Ertrag.

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Abstract Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be ‘team science’.http://deepblue.lib.umich.edu/bitstream/2027.42/134522/1/13742_2016_Article_117.pd

A comprehensive survey on deep active learning and its applications in medical image analysis

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. Additionally, we also highlight active learning works that are specifically tailored to medical image analysis. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis.Comment: Paper List on Github: https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysi

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Technology, Science and Culture

From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them

Advanced Analysis Methods for Large-Scale Structured Data

In the era of ’big data’, advanced storage and computing technologies allow people to build and process large-scale datasets, which promote the development of many fields such as speech recognition, natural language processing and computer vision. Traditional approaches can not handle the heterogeneity and complexity of some novel data structures. In this dissertation, we want to explore how to combine different tools to develop new methodologies in analyzing certain kinds of structured data, motivated by real-world problems. Multi-group design, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), has been undertaken by recruiting subjects based on their multi-class primary disease status, while some extensive secondary outcomes are also collected. Analysis by standard approaches is usually distorted because of the unequal sampling rates of different classes. In the first part of the dissertation, we develop a general regression framework for the analysis of secondary phenotypes collected in multi-group association studies. Our regression framework is built on a conditional model for the secondary outcome given the multi-group status and covariates and its relationship with the population regression of interest of the secondary outcome given the covariates. Then, we develop generalized estimation equations to estimate the parameters of interest. We use simulations and a large-scale imaging genetic data analysis of the ADNI data to evaluate the effect of the multi-group sampling scheme on standard genome-wide association analyses based on linear regression methods, while comparing it with our statistical methods that appropriately adjust for the multi-group sampling scheme. In the past few decades, network data has been increasingly collected and studied in diverse areas, including neuroimaging, social networks and knowledge graphs. In the second part of the dissertation, we investigate the graph-based semi-supervised learning problem with nonignorable nonresponses. We propose a Graph-based joint model with Nonignorable Missingness (GNM) and develop an imputation and inverse probability weighting estimation approach. We further use graph neural networks (GNN) to model nonlinear link functions and then use a gradient descent (GD) algorithm to estimate all the parameters of GNM. We propose a novel identifiability for the GNM model with neural network structures, and validate its predictive performance in both simulations and real data analysis through comparing with models ignoring or misspecifying the missingness mechanism. Our method can achieve up to 7.5% improvement than the baseline model for the document classification task on the Cora dataset. Predictions of Origin-Destination (OD) flow data is an important instrument in transportation studies. However, most existing methods ignore the network structure of OD flow data. In the last part of the dissertation, we propose a spatial-temporal origin-destination (STOD) model, with a novel CNN filter to learn the spatial features from the perspective of graphs and an attention mechanism to capture the long term periodicity. Experiments on a real customer request dataset with available OD information from a ride-sharing platform demonstrates the advantage of STOD in achieving a more accurate and stable prediction performance compared to some state-of-the-art methods.Doctor of Philosoph

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field