scholarly journals Neural ranking models for document retrieval

2021 ◽  
Author(s):  
Mohamed Trabelsi ◽  
Zhiyu Chen ◽  
Brian D. Davison ◽  
Jeff Heflin
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Document Retrieval ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Learning Models ◽  
Ranking Models ◽  
Information Retrieval Systems ◽  
Main Components ◽  
Ranking Tasks

AbstractRanking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a set of hand-crafted features. Recently, researchers have leveraged deep learning models in information retrieval. These models are trained end-to-end to extract features from the raw data for ranking tasks, so that they overcome the limitations of hand-crafted features. A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking. In this paper, we compare the proposed models in the literature along different dimensions in order to understand the major contributions and limitations of each model. In our discussion of the literature, we analyze the promising neural components, and propose future research directions. We also show the analogy between document retrieval and other retrieval tasks where the items to be ranked are structured documents, answers, images and videos.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals EyeHealer: A large-scale anterior eye segment dataset with eye structure and lesion annotations

2021 ◽  
Author(s):  
Wenjia Cai ◽  
Jie Xu ◽  
Ke Wang ◽  
Xiaohong Liu ◽  
Wenqin Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Image Annotation ◽  
Clinical Care ◽  
Significant Proportion ◽  
Anterior Segment ◽  
Future Research ◽  
Learning Models ◽  
Anterior Eye Segment ◽  
Segmentation Models

Abstract Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide, including diseases associated with corneal pathologies, anterior chamber abnormalities (e.g. blood or inflammation) and lens diseases. The construction of an automatic tool for the segmentation of anterior segment eye lesions will greatly improve the efficiency of clinical care. With research on artificial intelligence progressing in recent years, deep learning models have shown their superiority in image classification and segmentation. The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise, however, such data are relatively scarce in the domain of medicine. Herein, the authors developed a new medical image annotation system, called EyeHealer. It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level. Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation. The results showed that semantic segmentation models outperformed medical segmentation models. This paper describes the establishment of the system for automated classification and segmentation tasks. The dataset will be made publicly available to encourage future research in this area.

scholarly journals Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kinshuk Sengupta ◽  
Praveen Ranjan Srivastava
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Image Classification ◽  
Machine Learning Algorithms ◽  
Classification Task ◽  
Clinical Image ◽  
Prototype Model ◽  
Learning Models ◽  
Accuracy Measure ◽  
Quantum Machine Learning

Abstract Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

scholarly journals Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Yiftach Barash ◽  
Noa Cahan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pulmonary Embolism ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Future Research ◽  
Learning Models ◽  
Summary Receiver Operating Characteristic ◽  
Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals Disease classification in Solanum melongena using deep learning

2019 ◽  
Vol 17 (3) ◽  
pp. e0204 ◽  
Author(s):  
Krishnaswamy R. Aravind ◽  
Purushothaman Raja ◽  
Rajendran Ashiwin ◽  
Konnaiyar V. Mukesh
Keyword(s):  
Deep Learning ◽  
Tamil Nadu ◽  
Solanum Melongena ◽  
Disease Classification ◽  
Healthy Plant ◽  
Future Research ◽  
Learning Models ◽  
Cercospora Leaf Spot ◽  
Before And After ◽  
Augmentation Techniques

Aim of study: The application of pre-trained deep learning models, AlexNet and VGG16, for classification of five diseases (Epilachna beetle infestation, little leaf, Cercospora leaf spot, two-spotted spider mite and Tobacco Mosaic Virus (TMV)) and a healthy plant in Solanum melongena (brinjal in Asia, eggplant in USA and aubergine in UK) with images acquired from smartphones.Area of study: Images were acquired from fields located at Alangudi (Pudukkottai district), Tirumalaisamudram and Pillayarpatti (Thanjavur district) – Tamil Nadu, India.Material and methods: Most of earlier studies have been carried out with images of isolated leaf samples, whereas in this work the whole or part of the plant images were utilized for the dataset creation. Augmentation techniques were applied to the manually segmented images for increasing the dataset size. The classification capability of deep learning models was analysed before and after augmentation. A fully connected layer was added to the architecture and evaluated for its performance.Main results: The modified architecture of VGG16 trained with the augmented dataset resulted in an average validation accuracy of 96.7%. Despite the best accuracy, all the models were tested with sample images from the field and the modified VGG16 resulted in an accuracy of 93.33%.Research highlights: The findings provide a guidance for possible factors to be considered in future research relevant to the dataset creation and methodology for efficient prediction using deep learning models.

scholarly journals An Art of Review on Conceptual based Information Retrieval

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 21-31
Author(s):  
P. Mahalakshmi ◽  
N. Sabiyath Fathima
Keyword(s):  
Information Retrieval ◽  
Keyword Search ◽  
Semantic Representation ◽  
Research Work ◽  
Semantic Search ◽  
Future Research ◽  
Conceptual Information ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems

Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed.

Using deep learning with registry linked claims data to predict hospitalization during chemotherapy: Feasibility study.

2019 ◽  
Vol 37 (27_suppl) ◽  
pp. 270-270
Author(s):  
Laura Elizabeth Panattoni ◽  
Li Li ◽  
Catherine R. Fedorenko ◽  
Emily Silgard ◽  
Scott White ◽  
...  
Keyword(s):  
Deep Learning ◽  
Claims Data ◽  
Predictive Accuracy ◽  
Machine Learning Algorithms ◽  
Receiver Operating Curve ◽  
Future Research ◽  
Primary Tumor Site ◽  
Model Complex ◽  
Ed Visits ◽  
Chemotherapy Initiation

270 Background: Approximately half of cancer patients undergoing outpatient chemotherapy experience unplanned Emergency Department (ED) visits and Inpatient (IP) stays. Current machine learning algorithms that identify high-risk patients are based on pre-treatment variables which can not detect changes in risk over time. Deep learning recurrent neural networks can model complex longitudinal patient histories. This study tests the feasibility of using an interpretable recurrent neural network to predict a patient’s daily likelihood of ED and unplanned IP stays in the six months following chemotherapy initiation. Methods: Medicare and commercial claims data were linked with cancer registry records for patients in Washington State from 2011 to 2017. The study included patients diagnosed with any primary tumor site, excluding leukemia, and treated with chemotherapy. We used the Reverse Time Attention model (RETAIN) with a 1:10 case-control match and included registry elements; diagnoses, procedures, medication, and utilization pre-and post-chemotherapy initiation. Patients were randomly divided into internal training, validation, and test sets (75%, 10%, 15%). Model accuracy was measured by the areas under the receiver operating curve (ROC) and precision-recall curve (PRC), and the Youden sensitivity and specificity. Results: Of the 15,400 eligible patients; 4,037 (26.2%) visited the ED a median of 1 time (6,080 total visits); 5,116 (33.2%) had a median of 1 IP stay (7,839 total stays). Both models had good predictive accuracy: The top 20 predictors for ED visits included 5 chemotherapy regimes, 12 procedures, and 2 tumor characteristics; IP stays included all chemotherapy regimes. Conclusions: The promising performance of RETAIN supports the possibility of building a tool capable of estimating daily hospitalization risk. However, future research, particularly with alternative data sources, may be required to predict hospitalization in a real time clinical setting. [Table: see text]

A review of the use of inverted files for best match searching in information retrieval systems

1983 ◽  
Vol 6 (2-3) ◽  
pp. 59-66 ◽  
Author(s):  
Shirley A. Perry ◽  
Peter Willett
Keyword(s):  
Information Retrieval ◽  
Computational Efficiency ◽  
Critical Evaluation ◽  
Document Retrieval ◽  
Nearest Neighbour ◽  
Matching Function ◽  
Similarity Coefficients ◽  
Retrieval Systems ◽  
Search Heuristics ◽  
Information Retrieval Systems

The use of inverted files for the calculation of similarity coefficients and other types of matching function is discussed in the context of mechanised document retrieval systems. A critical evaluation is presented of a range of algorithms which have been described for the matching of documents with queries. Particular attention is paid to the computational efficiency of the various procedures, and improved search heuristics are given in some cases. It is suggested that the algorithms could be implemented sufficiently efficiently to permit the provision of nearest neighbour searching as a standard retrieval option.

scholarly journals Deep learning models for classification of gases detected by sensor arrays of artificial nose

2019 ◽  
Author(s):  
Ismael Araujo ◽  
Juan Gamboa ◽  
Adenilton Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Human Beings ◽  
Learning Models ◽  
Classification Problems ◽  
Artificial Nose ◽  
Learning Techniques

To recognize patterns that are usually imperceptible by human beings has been one of the main advantages of using machine learning algorithms The use of Deep Learning techniques has been promising to the classification problems, especially the ones related to image classification. The classification of gases detected by an artificial nose is one other area where Deep Learning techniques can be used to seek classification improvements. Succeeding in a classification task can result in many advantages to quality control, as well as to preventing accidents. In this work, it is presented some Deep Learning models specifically created to the task of gas classification.

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