Neda B. Marvasti

Nokia · SOC Beamer

By advancement in digital marketing, business-to-business (B2B) buyers carry out over half of the buying process through digital touchpoints before they establish any significant contact with the B2B seller. Knowing the buying stage of a potential buyer can bring a substantial advantage to the B2B seller given the complexity of the transaction and...

The increasing volume of medical image data, as well as the need for multi-center data consolidation for big data analytics, require computer-aided medical image annotation (CMIA). Majority of the methods proposed so far do not exploit inter-dependencies between annotations explicitly. They further limit their annotations at a higher level than dia...

In this dissertation, an iterative search and retrieval scheme to identify similar images from a database of 3-dimensional liver computed tomography (CT) images is proposed via utilizing the combination of lesion and liver related semantic features and patients' metadata. At each retrieval iteration, the lesion related concepts are annotated in a s...

The first Liver CT annotation challenge was organized during the 2014 Image-CLEF workshop held in Sheffield, UK. This challenge entailed the annotation of Liver CT scans to generate structured reports. This paper describes the motivations for this task, the training and test datasets, the evaluation methods, and discusses the approaches of the part...

Business-to-business (B2B) sellers need to enhance content marketing and analytics in an online environment. The challenge is that sellers have data but do not know how to utilize it. In this study, we develop a neural content model to match the content that B2B sellers are providing with the type of content that buyers are seeking. The model was t...

This paper focuses on the simulation of a buying process and further estimation of its hidden stages in online B2B markets via a proposed statistical modeling technique. In recent decades, business to business (B2B) buying has become more digital-centric and buyer-driven than before. More than half of the B2B buying process is carried out through...

Past medical cases, hence clinical experience, are invaluable resources in supporting clinical practice, research, and education. Medical professionals need to be able to exchange information about patient cases and explore them from subjective perspectives. This requires a systematic and flexible methodology to case representation for supporting t...

This paper presents an overview of the ImageCLEF 2015 evaluation campaign, an event that was organized as part of the CLEF labs 2015. ImageCLEF is an ongoing initiative that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to databases of images in various usage scenarios and domains. I...

This paper presents an overview of the ImageCLEF 2014 evaluation lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and medi...

In this paper, we propose an image block loss restoration method based on the notion of sparse representation. The sparsity pattern is exploited as side information to efficiently restore block losses, by iteratively imposing the constraints of spatial and transform domains on the corrupted image. Two novel features, including a pre-interpolation a...

Clinical experience sharing (CES) is a useful concept for both medical treatment and medical education purposes. One way of implementing CES is through the use of content based case retrieval (CBCR), where database of medical cases is browsed for case instances that are similar to the input query case. In this study, we introduce a new project call...

Vessel segmentation plays an important role in medical image analysis. Irrespective of the modality used, the common challenge in all vessel tracking methods is scale variability, in other words, the dependence on the size of the vessels, which is unknown a priori. Despite few approaches that attempts to perform scale selection and segmentation sim...

Noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. Restoration of the lost information of an image based on the existing information is the essence of inpainting. In this paper a new algorithm based on Sample and Hold interpolation and Iteration is proposed for reconstr...

In this paper, we propose a new image inpainting method based on the property that much of the image information in the transform domain is sparse. We add a redundancy to the original image by mapping the transform coefficients with small amplitudes to zero and the resultant sparsity pattern is used as the side information in the recovery stage. If...