In current medical images diagnosis, specialists take pictures of patients and search for the disease from the images. In particular, coronary angiography is used to determine coronary artery stenosis. This method is burdensome to the patient because it uses a contrast agent. Therefore, we attempt to determine coronary artery stenosis from non-contrast CT images. Then, we propose a method to classify the stenosis using a 3D convolution model (I3D). As a result of comparing the proposed method and a method using a 2D convolutional model, the proposed method showed higher performance in four metrics.

Coronary artery disease, one of the major causes of death in Japan, is said to be related to coronary artery calcification. However, health care workers are essential to examine only the heart when investigating coronary artery disease. Also, it is very difficult to extract only calcification of the heart from Non-Contrast CT. To our knowledge, 3D cardiac extraction in non-enhanced CT cardiac images has not been performed in previous studies. In addition, the estimation of calcification score from heart extraction has not been done in previous studies. Therefore, we performed calcium score classification from cardiac CT using 3D-ResNet, which is a 3D extension of ResNet, which is highly accurate in image classification. Therefore, in this study, we propose a cardiac detection method using deep learning to improve the accuracy of calcium scoring. We conducted experiments using a dataset for cardiac detection from chest CT images and a dataset for calcium scoring from chest CT scans. The results show that the proposed method improves the accuracy of calcium scoring compared to the method without cardiac extraction. As a result, we proposed a state-of-the-art highly accurate heart detection method that has never existed before.

Grounding, or localizing, a textual phrase in an image is a challenging problem that is integral to visual language understanding. Previous approaches to this task typically make use of candidate region proposals, where end performance depends on that of the region proposal method and additional computational costs are incurred. In this paper, we treat grounding as a regression problem and propose a method to directly identify the region referred to by a textual phrase, eliminating the need for external candidate region prediction. Our approach uses deep neural networks to combine image and text representations and refines the target region with attention models over both image subregions and words in the textual phrase. Despite the challenging nature of this task and sparsity of available data, in evaluation on the ReferIt dataset, our proposed method achieves a new state-of-the-art in performance of 37.26% accuracy, surpassing the previously reported best by over 5 percentage points. We find that combining image and text attention models and an image attention area-sensitive loss function contribute to substantial improvements.

3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods are compared.

With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies ( GWAS ) have proved useful as a method for exploring phenotypic associations with diseases. However, since genetic diseases are difficult to diagnose because of their low prevalence, large number, and broad diversity of symptoms, genetic disease patients are often misdiagnosed or experience long diagnostic delays. In this article, we propose a method for ranking genetic diseases for a set of clinical phenotypes. In this regard, we associate a phenotype-gene bipartite graph ( PGBG ) with a gene-disease bipartite graph ( GDBG ) by producing a phenotype-disease bipartite graph ( PDBG ), and we estimate the candidate weights of diseases. In our approach, all paths from a phenotype to a disease are explored by considering causative genes to assign a weight based on path frequency, and the phenotype is linked to the disease in a new PDBG. We introduce the Bidirectionally induced Importance Weight ( BIW ) prediction method to PDBG for approximating the weights of the edges of diseases with phenotypes by considering link information from both sides of the bipartite graph. The performance of our system is compared to that of other known related systems by estimating Normalized Discounted Cumulative Gain ( NDCG ), Mean Average Precision ( MAP ), and Kendall’s tau metrics. Further experiments are conducted with well-known TF · IDF , BM25 , and Jenson-Shannon divergence as baselines. The result shows that our proposed method outperforms the known related tool Phenomizer in terms of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it performs worse than Phenomizer in terms of Kendall’s tau-b metric at the top-10 ranks. It also turns out that our proposed method has overall better performance than the baseline methods.

This article describes a novel intelligent interface for attitude sensing in text driven by a robust computational tool for the analysis of fine-grained attitudes (emotions, judgments, and appreciations) expressed in text. The module responsible for textual attitude analysis was developed using a compositional linguistic approach based on the attitude-conveying lexicon, the analysis of syntactic and dependency relations between words in a sentence, the compositionality principle applied at various grammatical levels, the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. The performance of this module was evaluated on sentences from personal stories about life experiences. The developed web-based interface supports recognition of nine emotions, positive and negative judgments, and positive and negative appreciations conveyed in text. It allows users to adjust parameters, to enable or disable various functionality components of the algorithm, and to select the format of text annotation and attitude statistics visualization.

In this work, we propose a method for automatic analysis of attitude (affect, judgment, and appreciation) in sentiment words. The first stage of the proposed method is an automatic separation of unambiguous affective and judgmental adjectives from those that express appreciation or different attitudes depending on context. In our experiments with machine learning algorithms we employed three feature sets based on Pointwise Mutual Information, word-pattern co-occurrence, and minimal path length. The next stage of the proposed method is to estimate the potentials of miscellaneous adjectives to convey affect, judgment, and appreciation. Based on the sentences automatically collected for each adjective, the algorithm analyses the context of phrases that contain sentiment words by considering morphological tags, high-level concepts, and named entities, and then makes decisions about contextual attitude labels. Finally, the appraisal potentials of a word are calculated based on the number of sentences related to each type of attitude. Our two-stage method was evaluated on two data sets, and promising results were obtained. The performance of our method was also compared with the method from previous work.

We propose a method to rate the of news articles using three clues: (1) commonality of the contents of articles among different news publishers; (2) numerical agreement versus contradiction of numerical values reported in the articles; and (3) objectivity based on subjective speculative phrases and news sources. We tested this method on news stories taken from seven different news sites on the Web. The average agreement between the system-produced credibility and the manual judgments of three human assessors on the 52 sample articles was 69.1%. The limitations of the current approach and future directions are discussed.

Existing data embedding algorithms for polygonal meshes and their attributes cannot be applied to the majority of (geometric) computer aided design (CAD) applications for two major reasons. First, these CAD systems employ parametric curves and surfaces, not polygonal meshes, as their main shape-defining primitives. Second, most CAD applications do not tolerate modifications of model topology and/or geometry that are introduced by existing data embedding algorithms. We propose a new data embedding algorithm for non-uniform rational B-spline (NURBS) curves and surfaces, which employs rational linear reparameterization for embedding messages. The algorithm exactly preserves the shape, that is, the geometry and topology of its embedding targets. Furthermore, it preserves the data size of the model. We consider how these two properties, preservation of shape and preservation of data size, can be significant with regard to the use of data embedding in CAD applications. In addition to the shape- and data size-preserving data embedding algorithm for NURBS curves and surfaces, we outline additional methods for embedding data in various types of parametric curves and surfaces.

This paper discusses techniques for embedding data into three-dimensional (3-D) polygonal models of geometry. Given objects consisting of points, lines, (connected) polygons, or curved surfaces, the algorithms described in produce polygonal models with data embedded into either their vertex coordinates, their vertex topology (connectivity), or both. Such data embedding can be used, for example, for copyright notification, copyright protection, theft deterrence, and inventory of 3-D polygonal models. A description of the background and requirements is followed by a discussion of where, and by what fundamental methods, data can be embedded into 3-D polygonal models. The paper then presents several data embedding algorithms, with examples, based on these fundamental methods. By means of these algorithms and examples, we show that the embedding of data into 3-D polygonal models is a practicable technique.

The advantages of digital media such as the Internet and CD-ROMs lie in the fact that their contents are easy to duplicate, edit, and distribute.These advantages, however, are double-edged swords, because they also facilitate unauthorized use of such contents.Data embedding, which places information into the contents themselves, is an approach to address this issue.Embedded information can be used, for example, for copyright protection, theft deterrence, and inventory.This paper discusses our work on embedding data into three-dimensional (3D) polygonal models of geometry.Given objects consisting of points, lines, polygons, or curved surfaces, the data embedding algorithms described in this paper produce polygonal models with data embedded.Data are placed into 3D polygonal models by modifying either their vertex coordinates, their vertex topology (connectivity), or both.A brief review of related work and a description of the requirements of data embedding is followed by a discussion of where, and by what fundamental methods, data can be embedded into 3D polygonal models.The paper then presents data-embedding algorithms, with examples, based on these fundamental methods.