1,720,970 research outputs found
Deep Learning Approaches Targeting Radiological Images
No full textArtificial Intelligence (AI) algorithms have remarkably improved their performance in the recent years
in various domains, thanks to the introduction of deep learning approaches. Indeed they have shown a tremendous potential when solving tasks involving image analysisThe problem of deep learning is its requirement for huge datasets, nonetheless, DL approaches have proved to be helpful in the domain of medical imaging as well. Automated segmentation and classification in different biomedical tasks have proven to be faster and more cost effective.
In this thesis we study deep learning approaches used for segmentation and classification of different radiological images mainly CT Scans, MRI Scans and CXR images. In particular, we explored some issues like the multi-modality, and the small dataset problem
We first discuss about how the small datasets can be exploited to improve the performance of the deep model in the proposed architectures and then in the next work we train the model with multi modal data consisting of both CT and MRI images together and consider the corresponding opposite modality of CT and MRI as missing data problem. We use Cycle-GAN to generate the synthetic data for the missing data and further train the model with original and synthetic data together.
Then we focus on the classification of COVID exploiting the multi-modality data available. We proposed an architecture that is capable of handling multi modal data and extract feature representation from available modalities before concatenation and further use them for final classification. Then we exploit joint learning to train a small dataset from scratch.
Finally, this thesis concludes with open questions that may benefit from future work. This thesis demonstrate the potential role of CNNs to address the tasks of segmentation and classification
Multimodal Segmentation of Medical Images with Heavily Missing Data
No full textAn important aim of research in medical imaging is the development of computer aided diagnosis (CAD) systems. A fundamental step in these systems is the image segmentation and convolutional neural networks (CNNs) are becoming the most commonly used approach to solve this task. However, despite their great power, in this domain CNNs are limited in their potential performance by the usually small amount of data [1]. Computed tomography (CT) and magnetic resonance imaging (MRI) scans are often used to examine the internal structure of human body and have their own unique properties and limitations. As a common practice, the investigations are usually done on a single modality, nonetheless, the simultaneous analysis of multiple modalities can significantly boost the segmentation accuracy. However, obtaining multiple imaging modalities for the same subject is very unlikely. In this paper we investigate the possibility of generating a multimodal CT-MRI representation for a segmentation task starting from a single modality, either CT or MRI. We considered this as a missing data problem, hence, we designed a pipeline where a CycleGAN was used to generate the missing modality. The synthetic modality was then paired with the real one to perform the required segmentation taking advantage of the multimodal representation and the augmented training dataset. To test the system we used two unrelated labeled datasets, one with CT data and the other one with MRI data. Results show that data enrichment with synthetic modalities improves the segmentation performance
Multiple Organs Segmentation in Abdomen CT Scans Using a Cascade of CNNs
No full textAutomatic organ segmentation is a vital prerequisite of many clinical application in radiology. The anatomical variability of organs in the abdomen makes it difficult for many methods to obtain good segmentations for all organs. In this paper, we present a particular ensemble of convolutional neural networks, combining technologies that analyze the images with either a local or a global perspective. In particular, we implemented a cascade of models combining the advantages of using local and global processing. We have evaluated our proposed system on CT scan of 30 subjects in a nested cross-validation framework, showing a significant performance improvement if compared with state-of-the-art methods
Organ Segmentation with Recursive Data Augmentation for Deep Models
No full textThe precise segmentation of organs from computed tomography is a fundamental and pivotal task for correct diagnosis and proper treatment of diseases. Neural network models are widely explored for their promising performance in the segmentation of medical images. However, the small dimension of available datasets is affecting the biomedical imaging domain significantly and has a huge impact in training of deep learning models. In this paper we try to address this issue by iteratively augmenting the dataset with auxiliary task-based information. This is obtained by introducing a recursive training approach, where a new set of segmented images is generated at each iteration and then concatenated with the original input data as organ attention maps. In the experimental evaluation two different datasets were tested and the results produced from the proposed approach have shown significant improvements in organ segmentation as compared to a standard non-recursive approach
Geodesic Clustering of Positive Definite Matrices For Classification of Mental Disorder Using Brain Functional Connectivity
No full textFunctional Magnetic Resonance Imaging (fMRI) is a commonly used technique to evaluate brain activity, and can be used to distinguish patients from healthy controls in a variety of diseases. In this work, we present a two-step approach to discriminate healthy subjects against those affected by either Autism Spectrum Disorder or Schizophrenia on the basis of their connectivity patterns. We exploited the property that connectivity patterns described by positive definite matrices define a Riemannian manifold. In this framework, to generate a vector representation used in the classification task, we performed a geodesic clustering of the connectivity matrices. Cluster centroids were then used as a dictionary allowing to encode all subjects graphs as vectors of geodesic distances. A linear Support Vector Machine was then used to classify subjects. To show the advantage of using geodesic distances for this problem, the same analysis was conducted using a Euclidean metric. Experiments show that employing Euclidean distances leads to a lower classification performance and possibly to the definition of the wrong number of clusters, whereas geodesic clustering results in a significantly improved accuracy
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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