1,721,011 research outputs found
Deep learning for automated polyp detection and localization in colonoscopy
Get PDFGastrointestinal (GI) tract comprises organs from mouth to anus. Multiple diseases can occur in the GI tract. Among the different diseases in the
digestive system, the most commonly found cancer in the gastrointestinal tract are oesophagus cancer, stomach cancer and colorectal cancer (CRC).
Amongst them, CRC is the third most commonly caused cancer in terms of incidence and the second most commonly leading cause of cancer-related
death worldwide. Norway specifically has the highest occurrence of colon cancer worldwide. Prior detection is a crucial factor to improve chances of survival, prognosis and timely treatment. Recently, there has been significant progress made in the field of medical image analysis through the development of Computer-aided diagnosis systems. CADx systems utilizing machine learning algorithms, specifically deep learning methods have produced excellent object detection models for automatic detection of abnormalities in medicine. In this respect, this thesis explores the possibility of implementing recent state-of-the-art deep learning models for automated polyp detection. The You Only Look Once (YOLO) family of models have demonstrated good results for object detection tasks, especially YOLOv4. Lately, YOLOv5, YOLO Representation (YOLOR) and Paddle- Paddle YOLO (PP-YOLO) were released which have shown even an improved performance over the earlier version YOLOv4. These real-time detection models were used together with the Kvasir-SEG dataset and BKAI-IGH NeoSmall-Polyp that contains several GI tract images with annotations to detect CRC precursor lesions called polyps. The models were compared to previous state-of-the-art models using metrics mAP, precision and recall. The models showed improved performance for mAP value. Additionally, we also achieved real-time processing speed with our models. However, the models achieved satisfactory accuracy for detection which indicates that there is still future scope for improvement. The experimental results presented in the
study show that the presented method can be a strong benchmark for the development of an automated polyp detection system in real-world clinical applications.publishedVersio
Deep learning for automated polyp detection and localization in colonoscopy
No full textGastrointestinal (GI) tract comprises organs from mouth to anus. Multiple diseases can occur in the GI tract. Among the different diseases in the
digestive system, the most commonly found cancer in the gastrointestinal tract are oesophagus cancer, stomach cancer and colorectal cancer (CRC).
Amongst them, CRC is the third most commonly caused cancer in terms of incidence and the second most commonly leading cause of cancer-related
death worldwide. Norway specifically has the highest occurrence of colon cancer worldwide. Prior detection is a crucial factor to improve chances of survival, prognosis and timely treatment. Recently, there has been significant progress made in the field of medical image analysis through the development of Computer-aided diagnosis systems. CADx systems utilizing machine learning algorithms, specifically deep learning methods have produced excellent object detection models for automatic detection of abnormalities in medicine. In this respect, this thesis explores the possibility of implementing recent state-of-the-art deep learning models for automated polyp detection. The You Only Look Once (YOLO) family of models have demonstrated good results for object detection tasks, especially YOLOv4. Lately, YOLOv5, YOLO Representation (YOLOR) and Paddle- Paddle YOLO (PP-YOLO) were released which have shown even an improved performance over the earlier version YOLOv4. These real-time detection models were used together with the Kvasir-SEG dataset and BKAI-IGH NeoSmall-Polyp that contains several GI tract images with annotations to detect CRC precursor lesions called polyps. The models were compared to previous state-of-the-art models using metrics mAP, precision and recall. The models showed improved performance for mAP value. Additionally, we also achieved real-time processing speed with our models. However, the models achieved satisfactory accuracy for detection which indicates that there is still future scope for improvement. The experimental results presented in the
study show that the presented method can be a strong benchmark for the development of an automated polyp detection system in real-world clinical applications
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Machine Learning-based Classification, Detection, and Segmentation of Medical Images
Get PDFGastrointestinal tract (GI) cancers are among the most common types of cancers worldwide. In particular, colorectal cancer (CRC) is the most lethal in terms of number of incidences and mortality (third most common cause of cancer and the second common cause of cancer-related deaths). Colonoscopy is the gold standard for screening patients for CRC. During the colonoscopy, gastroenterologists examine the large bowel, detect precancerous abnormal tissue growths like polyps and remove them through the scope if necessary. Although colonoscopy is considered the gold standard, it is an operator-dependent procedure. Previous research has shown large missing rates for GI abnormalities, e.g., polyp miss detection is around 22\%-28\%. Early detection of GI lesions and cancers at the curable stage can help reduce the mortality rate. The development of automated, accurate, and efficient methods for the detection of the GI cancers could benefit both gastroenterologists and patients. In addition, if integrated into screening programs, an automatic analysis could improve overall GI endoscopy quality.
The medical field is becoming more interdisciplinary, and the importance of medical image data is increasing rapidly. Medical image analysis can play a central role in disease detection, diagnosis, and treatment. With the increasing number of medical images, there is enormous potential to improve the screening quality. Deep learning (DL), in particular, convolutional neural network (CNN) based models have tremendous potential to automate and enhance the medical image analysis procedure and provide an accurate diagnosis. The automated analysis of the medical images could reduce the burden of the medical experts and provide quality and accessible healthcare to a larger population. In medical imaging, classification, detection, and semantic segmentation tasks are crucial for clinical practice. The development of accurate and efficient computer aided diagnosis (cadx) or computer aided detection (cade) models can help to identify the abnormalities at an early stage and can act as a third eye for the doctors.
To this end, we have studied and designed machine learning (ML) and DL based architectures for GI tract disease classification, detection, and segmentation. Our designed architectures can classify different types of GI tract findings and abnormalities accurately with high performance. Our contribution towards the development of CADe models for automated polyp detection showed improved performance. Out of three different medical imaging tasks, semantic segmentation of medical imaging data plays a significant role in extracting meaningful information from images by classifying each pixel and segmenting it by class. Using the GI case scenario, we have mainly worked on polyp segmentation and proposed and evaluated different automated polyp segmentation architectures. We have also built architectures for surgical instrument segmentation that showed high performance and real-time speed.
We have collected, annotated, and released several open-access datasets such as HyperKvasir, KvasirCapsule, PolypGen, Kvasir-SEG, Kvasir-instrument, and KvasirCapsule-SEG in collaboration with hospitals in Norway and abroad to address the lack of datasets in the field. We have devised several medical image segmentation architectures (for example, ResUNet++, DoubleU-Net, and ResUNet + CRF + TTA) that provided improved results with the publicly available datasets. Beside that, we have also designed architectures that have the capability of segmenting polyps in real-time with high frame per second (FPS) (for example, ColonSegNet, NanoNet, PNS-Net, and DDANet). Moreover, we performed extensive studies on the generalizability of our models on public datasets, and by creating a dataset consisting of data from different hospitals, we allow multi-center cross dataset testing. Our results prove that proposed DL based CADx systems might be of great assistance to clinicians in the future
Variations on the Author
Get PDF“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
Get PDFWe 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
Get PDFWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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