Academic literature on the topic 'BREAKHIS DATASET'
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Journal articles on the topic "BREAKHIS DATASET"
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Joshi, Shubhangi A., Anupkumar M. Bongale, P. Olof Olsson, Siddhaling Urolagin, Deepak Dharrao, and Arunkumar Bongale. "Enhanced Pre-Trained Xception Model Transfer Learned for Breast Cancer Detection." Computation 11, no. 3 (March 13, 2023): 59. http://dx.doi.org/10.3390/computation11030059.
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Early detection and timely breast cancer treatment improve survival rates and patients’ quality of life. Hence, many computer-assisted techniques based on artificial intelligence are being introduced into the traditional diagnostic workflow. This inclusion of automatic diagnostic systems speeds up diagnosis and helps medical professionals by relieving their work pressure. This study proposes a breast cancer detection framework based on a deep convolutional neural network. To mine useful information about breast cancer through breast histopathology images of the 40× magnification factor that are publicly available, the BreakHis dataset and IDC(Invasive ductal carcinoma) dataset are used. Pre-trained convolutional neural network (CNN) models EfficientNetB0, ResNet50, and Xception are tested for this study. The top layers of these architectures are replaced by custom layers to make the whole architecture specific to the breast cancer detection task. It is seen that the customized Xception model outperformed other frameworks. It gave an accuracy of 93.33% for the 40× zoom images of the BreakHis dataset. The networks are trained using 70% data consisting of BreakHis 40× histopathological images as training data and validated on 30% of the total 40× images as unseen testing and validation data. The histopathology image set is augmented by performing various image transforms. Dropout and batch normalization are used as regularization techniques. Further, the proposed model with enhanced pre-trained Xception CNN is fine-tuned and tested on a part of the IDC dataset. For the IDC dataset training, validation, and testing percentages are kept as 60%, 20%, and 20%, respectively. It obtained an accuracy of 88.08% for the IDC dataset for recognizing invasive ductal carcinoma from H&E-stained histopathological tissue samples of breast tissues. Weights learned during training on the BreakHis dataset are kept the same while training the model on IDC dataset. Thus, this study enhances and customizes functionality of pre-trained model as per the task of classification on the BreakHis and IDC datasets. This study also tries to apply the transfer learning approach for the designed model to another similar classification task.
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Xu, Xuebin, Meijuan An, Jiada Zhang, Wei Liu, and Longbin Lu. "A High-Precision Classification Method of Mammary Cancer Based on Improved DenseNet Driven by an Attention Mechanism." Computational and Mathematical Methods in Medicine 2022 (May 14, 2022): 1–14. http://dx.doi.org/10.1155/2022/8585036.
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Cancer is one of the major causes of human disease and death worldwide, and mammary cancer is one of the most common cancer types among women today. In this paper, we used the deep learning method to conduct a preliminary experiment on Breast Cancer Histopathological Database (BreakHis); BreakHis is an open dataset. We propose a high-precision classification method of mammary based on an improved convolutional neural network on the BreakHis dataset. We proposed three different MFSCNET models according to the different insertion positions and the number of SE modules, respectively, MFSCNet A, MFSCNet B, and MFSCNet C. We carried out experiments on the BreakHis dataset. Through experimental comparison, especially, the MFSCNet A network model has obtained the best performance in the high-precision classification experiments of mammary cancer. The accuracy of dichotomy was 99.05% to 99.89%. The accuracy of multiclass classification ranges from 94.36% to approximately 98.41%.Therefore, it is proved that MFSCNet can accurately classify the mammary histological images and has a great application prospect in predicting the degree of tumor. Code will be made available on http://github.com/xiaoan-maker/MFSCNet.
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Ogundokun, Roseline Oluwaseun, Sanjay Misra, Akinyemi Omololu Akinrotimi, and Hasan Ogul. "MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors." Sensors 23, no. 2 (January 6, 2023): 656. http://dx.doi.org/10.3390/s23020656.
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Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients’ recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model “MobileNet-SVM”, which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.
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Ukwuoma, Chiagoziem C., Md Altab Hossain, Jehoiada K. Jackson, Grace U. Nneji, Happy N. Monday, and Zhiguang Qin. "Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head." Diagnostics 12, no. 5 (May 5, 2022): 1152. http://dx.doi.org/10.3390/diagnostics12051152.
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Introduction and Background: Despite fast developments in the medical field, histological diagnosis is still regarded as the benchmark in cancer diagnosis. However, the input image feature extraction that is used to determine the severity of cancer at various magnifications is harrowing since manual procedures are biased, time consuming, labor intensive, and error-prone. Current state-of-the-art deep learning approaches for breast histopathology image classification take features from entire images (generic features). Thus, they are likely to overlook the essential image features for the unnecessary features, resulting in an incorrect diagnosis of breast histopathology imaging and leading to mortality. Methods: This discrepancy prompted us to develop DEEP_Pachi for classifying breast histopathology images at various magnifications. The suggested DEEP_Pachi collects global and regional features that are essential for effective breast histopathology image classification. The proposed model backbone is an ensemble of DenseNet201 and VGG16 architecture. The ensemble model extracts global features (generic image information), whereas DEEP_Pachi extracts spatial information (regions of interest). Statistically, the evaluation of the proposed model was performed on publicly available dataset: BreakHis and ICIAR 2018 Challenge datasets. Result: A detailed evaluation of the proposed model’s accuracy, sensitivity, precision, specificity, and f1-score metrics revealed the usefulness of the backbone model and the DEEP_Pachi model for image classifying. The suggested technique outperformed state-of-the-art classifiers, achieving an accuracy of 1.0 for the benign class and 0.99 for the malignant class in all magnifications of BreakHis datasets and an accuracy of 1.0 on the ICIAR 2018 Challenge dataset. Conclusion: The acquired findings were significantly resilient and proved helpful for the suggested system to assist experts at big medical institutions, resulting in early breast cancer diagnosis and a reduction in the death rate.
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Mohanakurup, Vinodkumar, Syam Machinathu Parambil Gangadharan, Pallavi Goel, Devvret Verma, Sameer Alshehri, Ramgopal Kashyap, and Baitullah Malakhil. "Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network." Computational Intelligence and Neuroscience 2022 (July 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/8517706.
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Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDBN uses the lead backbone feature maps to identify objects. It feeds high-level output features from previous backbones into the next backbone in a stepwise way. We show that most contemporary detectors can easily include CDBN to improve performance achieved mAP improvements ranging from 1.5 to 3.0 percent on the breast cancer histopathological image classification (BreakHis) dataset. Experiments have also shown that instance segmentation may be improved. In the BreakHis dataset, CDBN enhances the baseline detector cascade mask R-CNN (mAP = 53.3). The proposed CDBN detector does not need pretraining. It creates high-level traits by combining low-level elements. This network is made up of several identical backbones that are linked together. The composite dilated backbone considers the linked backbones CDBN.
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Nahid, Abdullah-Al, Mohamad Ali Mehrabi, and Yinan Kong. "Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering." BioMed Research International 2018 (2018): 1–20. http://dx.doi.org/10.1155/2018/2362108.
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Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F-Measure value is achieved on both the 40x and 100x datasets.
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Sun, Yixin, Lei Wu, Peng Chen, Feng Zhang, and Lifeng Xu. "Using deep learning in pathology image analysis: A novel active learning strategy based on latent representation." Electronic Research Archive 31, no. 9 (2023): 5340–61. http://dx.doi.org/10.3934/era.2023271.
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<abstract><p>Most countries worldwide continue to encounter a pathologist shortage, significantly impeding the timely diagnosis and effective treatment of cancer patients. Deep learning techniques have performed remarkably well in pathology image analysis; however, they require expert pathologists to annotate substantial pathology image data. This study aims to minimize the need for data annotation to analyze pathology images. Active learning (AL) is an iterative approach to search for a few high-quality samples to train a model. We propose our active learning framework, which first learns latent representations of all pathology images by an auto-encoder to train a binary classification model, and then selects samples through a novel ALHS (Active Learning Hybrid Sampling) strategy. This strategy can effectively alleviate the sample redundancy problem and allows for more informative and diverse examples to be selected. We validate the effectiveness of our method by undertaking classification tasks on two cancer pathology image datasets. We achieve the target performance of 90% accuracy using 25% labeled samples in Kather's dataset and reach 88% accuracy using 65% labeled data in BreakHis dataset, which means our method can save 75% and 35% of the annotation budget in the two datasets, respectively.</p></abstract>
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Istighosah, Maie, Andi Sunyoto, and Tonny Hidayat. "Breast Cancer Detection in Histopathology Images using ResNet101 Architecture." sinkron 8, no. 4 (October 1, 2023): 2138–49. http://dx.doi.org/10.33395/sinkron.v8i4.12948.
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Cancer is a significant challenge in many fields, especially health and medicine. Breast cancer is among the most common and frequent cancers in women worldwide. Early detection of cancer is the main step for early treatment and increasing the chances of patient survival. As the convolutional neural network method has grown in popularity, breast cancer can be easily identified without the help of experts. Using BreaKHis histopathology data, this project will assess the efficacy of the CNN architecture ResNet101 for breast cancer image classification. The dataset is divided into two classes, namely 1146 malignant and 547 benign. The treatment of data preprocessing is considered. The implementation of data augmentation in the benign class to obtain data balance between the two classes and prevent overfitting. The BreaKHis dataset has noise and uneven color distribution. Approaches such as bilateral filtering, image enhancement, and color normalization were chosen to enhance image quality. Adding flatten, dense, and dropout layers to the ResNet101 architecture is applied to improve the model performance. Parameters were modified during the training stage to achieve optimal model performance. The Adam optimizer was used with a learning rate 0.0001 and a batch size of 32. Furthermore, the model was trained for 100 epochs. The accuracy, precision, recall, and f1-score results are 98.7%, 98.73%, 98.7%, and 98.7%, respectively. According to the results, the proposed ResNet101 model outperforms the standard technique as well as other architectures.
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Li, Lingxiao, Niantao Xie, and Sha Yuan. "A Federated Learning Framework for Breast Cancer Histopathological Image Classification." Electronics 11, no. 22 (November 16, 2022): 3767. http://dx.doi.org/10.3390/electronics11223767.
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Quantities and diversities of datasets are vital to model training in a variety of medical image diagnosis applications. However, there are the following problems in real scenes: the required data may not be available in a single institution due to the number of patients or the type of pathology, and it is often not feasible to share patient data due to medical data privacy regulations. This means keeping private data safe is required and has become an obstacle in fusing data from multi-party to train a medical model. To solve the problems, we propose a federated learning framework, which allows knowledge fusion achieved by sharing the model parameters of each client through federated training rather than sharing data. Based on breast cancer histopathological dataset (BreakHis), our federated learning experiments achieve the expected results which are similar to the performances of the centralized learning and verify the feasibility and efficiency of the proposed framework.
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Burrai, Giovanni P., Andrea Gabrieli, Marta Polinas, Claudio Murgia, Maria Paola Becchere, Pierfranco Demontis, and Elisabetta Antuofermo. "Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis." Animals 13, no. 9 (May 6, 2023): 1563. http://dx.doi.org/10.3390/ani13091563.
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Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset—namely CMTD—of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.
Dissertations / Theses on the topic "BREAKHIS DATASET"
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Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.
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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM.
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SAADIZADEH, SAMAN. "SIGNIFICANTLY ACCURATE SYSTEM FOR BREAST CANCER MALIGNANCY OR BENIGN CLASSIFICATION." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19429.
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Breast cancer happens to one out of eight females worldwide. It is the most elevated reason for
cancer malignancy deadliness among ladies. It is identified by finding the cancerous cells in
breast tissue. Novel techniques in medical image processing utilized histopathology dataset
images taken by an advanced microscope, and then disintegrate the images by applying various
algorithms and techniques. Artificial Intelligence methods are presently being applied for
processing pathological imagery and tools. Here in the project work, we concentrate on
building up the capability of computer-aided diagnosis (CAD) to anticipate the severity of
cancerous cells. Common cancerous cell detecting is a tedious process and involves the fault of
physicians, to this end we can use computer-aided detection (CAD) system to reduce the fault
and obtain the more acceptable outcome in comparison to a common pathological detection
system. Here we are comparing, our framework with the other three machine learning
frameworks in breast image segmentation and classification on a well-known dataset (BreakHis)
trial arrangement. Classification in deep neural network mainly utilize feature extraction by the
means of convolutional neural network and then by embedding a fully connected network, the
result would be an acceptable output. Deep learning has a vast amount of functionality in
medical image processing without any need for supervision of any professional person during
the process and the procedure can be done automatically. Here in our project we train a bunch
of histopathology images through a convolutional neural network and obtain accuracy in
prediction more than 92%.
Books on the topic "BREAKHIS DATASET"
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Tan, Yeling. Disaggregating China, Inc. Cornell University Press, 2021. http://dx.doi.org/10.7591/cornell/9781501759635.001.0001.
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Set in the aftermath of China's entry into the World Trade Organization (WTO), this book questions the extent to which the liberal internationalist promise of membership has been fulfilled in China. The book unpacks the policies that various Chinese government actors adopted in response to WTO rules and shows that rather than disciplining the state, WTO entry provoked a divergence of policy responses across different parts of the complex party-state. It argues that these responses draw from three competing strategies of economic governance: market-substituting (directive), market-shaping (developmental), and market-enhancing (regulatory). The book uses innovative web-scraping techniques to assemble an original dataset of over 43,000 Chinese industry regulations, identifying policies associated with each strategy. Combining textual analysis with industry data, in-depth case studies, and field interviews with industry representatives and government officials, the book demonstrates that different Chinese state actors adopted different logics of adjustment to respond to the common shock of WTO accession. This policy divergence originated from a combination of international and domestic forces. The book breaks open the black box of the Chinese state, explaining why WTO rules, usually thought to commit states to international norms, instead provoked responses that the architects of those rules neither expected nor wanted.
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Lyall, Jason. Divided Armies. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691192444.001.0001.
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How do armies fight and what makes them victorious on the modern battlefield? This book challenges long-standing answers to this classic question by linking the fate of armies to their levels of inequality. Introducing the concept of military inequality, the book demonstrates how a state's prewar choices about the citizenship status of ethnic groups within its population determine subsequent battlefield performance. Treating certain ethnic groups as second-class citizens, either by subjecting them to state-sanctioned discrimination or, worse, violence, undermines interethnic trust, fuels grievances, and leads victimized soldiers to subvert military authorities once war begins. The higher an army's inequality, the book finds, the greater its rates of desertion, side-switching, casualties, and use of coercion to force soldiers to fight. The book draws on Project Mars, a new dataset of 250 conventional wars fought since 1800, to test this argument. Project Mars breaks with prior efforts by including overlooked non-Western wars while cataloguing new patterns of inequality and wartime conduct across hundreds of belligerents. The book also marshals evidence from nine wars, ranging from the Eastern Fronts of World Wars I and II to less familiar wars in Africa and Central Asia, to illustrate inequality's effects. Sounding the alarm on the dangers of inequality for battlefield performance, the book offers important lessons about warfare over the past two centuries—and for wars still to come.
Book chapters on the topic "BREAKHIS DATASET"
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Agarwal, Pinky, Anju Yadav, and Pratistha Mathur. "Breast Cancer Prediction on BreakHis Dataset Using Deep CNN and Transfer Learning Model." In Data Engineering for Smart Systems, 77–88. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2641-8_8.
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Schirmer, Pascal A., and Iosif Mporas. "Binary versus Multiclass Deep Learning Modelling in Energy Disaggregation." In Springer Proceedings in Energy, 45–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_6.
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AbstractThis paper compares two different deep-learning architectures for the use in energy disaggregation and Non-Intrusive Load Monitoring. Non-Intrusive Load Monitoring breaks down the aggregated energy consumption into individual appliance consumptions, thus detecting device operation. In detail, the “One versus All” approach, where one deep neural network per appliance is trained, and the “Multi-Output” approach, where the number of output nodes is equal to the number of appliances, are compared to each other. Evaluation is done on a state-of-the-art baseline system using standard performance measures and a set of publicly available datasets out of the REDD database.
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Dellmuth, Lisa. "EU Spending Effects on Regional Well-Being." In Is Europe Good for You?, 77–98. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529217469.003.0005.
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This chapter examines the effects of EU spending on regional well-being. In doing so, the chapter tests the observable implications of the theory put forward in Chapter 3 about the conditions under which EU spending can enhance well-being. It makes the case for using a quantitative approach, which is uniquely suitable for estimating EU spending effects because it takes into account potential endogenous effects, temporal factors and structural breaks. proceeds with an explanatory analysis of. Drawing on the quantitative dataset introduced in Chapter 4, this chapter presents and discusses two main results. The first result is that EU social spending enhances employment and unemployment outcomes, but only in the rich regions. By contrast, there is no robust evidence of social spending effects on youth activity or public health. The second main finding is that EU social investments exacerbate income inequality in poor regions.
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Thomas, D. J., B. D. Sutton, J. W. Ferguson, and E. Price. "Spatially Resolved Detonation Pressure Data From Rate Sticks." In Future Developments in Explosives and Energetics, 105–19. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781788017855-00105.
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Quantifying how, and if, the pressure varies across a propagating detonation wave front is a parameter of interest. To investigate this phenomenon three 30 mm diameter rate stick experiments were performed. These used a TATBbased explosive with a density of 1.903 ± 0.003 g cm-3. Rate sticks measure the velocity of detonation (VoD) and detonation wave shape of an explosive in the same experiment, typically using time of arrival diagnostics and a streak camera. In this work instead of using a streak camera to interrogate the shape of the detonation wave as it breaks out from the end of the cylinder, an array of 15 Heterodyne velocimetry (HetV) probes was fielded. Although HetV probes have lower spatiotemporal resolution than a streak camera, this setup meant it was possible to determine the VoD, wave shape and pressure in the same experiment. The pressure was inferred from the velocity measurements made by each of the probes through a Kel-F 800 window. The probe jump-off times were combined with the VoD, measured using electrical time of arrival diagnostics, to determine the wave shape. The data from these rate sticks will be presented and comparisons made to previous datasets.
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Thomas, D. J., B. D. Sutton, J. W. Ferguson, and E. Price. "Spatially Resolved Detonation Pressure Data From Rate Sticks." In Future Developments in Explosives and Energetics, 105–19. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/9781839162350-00105.
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Quantifying how, and if, the pressure varies across a propagating detonation wave front is a parameter of interest. To investigate this phenomenon three 30 mm diameter rate stick experiments were performed. These used a TATBbased explosive with a density of 1.903 ± 0.003 g cm-3. Rate sticks measure the velocity of detonation (VoD) and detonation wave shape of an explosive in the same experiment, typically using time of arrival diagnostics and a streak camera. In this work instead of using a streak camera to interrogate the shape of the detonation wave as it breaks out from the end of the cylinder, an array of 15 Heterodyne velocimetry (HetV) probes was fielded. Although HetV probes have lower spatiotemporal resolution than a streak camera, this setup meant it was possible to determine the VoD, wave shape and pressure in the same experiment. The pressure was inferred from the velocity measurements made by each of the probes through a Kel-F 800 window. The probe jump-off times were combined with the VoD, measured using electrical time of arrival diagnostics, to determine the wave shape. The data from these rate sticks will be presented and comparisons made to previous datasets.
Conference papers on the topic "BREAKHIS DATASET"
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MAYOUF, MOUNA SABRINE, and FLORENCE DUPIN DE SAINT-Cyr. "Curriculum Incremental Deep Learning on BreakHis DataSet." In ICCTA 2022: 2022 8th International Conference on Computer Technology Applications. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3543712.3543747.
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Santos, Stefane A., Andressa G. Moreira, and Ialis C. P. Junior. "Análise comparativa da influência de otimizadores no desempenho de uma CNN para detecção do câncer de mama." In Escola Regional de Computação Ceará, Maranhão, Piauí. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/ercemapi.2021.17901.
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O campo da inteligência artificial (IA) apresenta notáveis avanços na medicina. Estudos analisam a aplicação de Redes Neurais Convolucionais para a detecção de câncer de mama. Neste artigo, é realizada uma análise comparativa entre os métodos de otimização (Adam, Adadelta, Adagrad, Adamax, Nadam, RMSprop) aplicados a uma arquitetura VggNet16 para a classificação de neoplasias em imagens histopatológicas. Os experimentos foram realizados com a criação de modelos para os fatores de ampliação (40x, 100x, 200x, 400x) das imagens extraídas do dataset BreakHis. O otimizador Adam obteve o melhor resultado para o conjunto de imagens, especificamente na base 400x.
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Freitas, Mario Pinto, Marcos Gabriel Mendes Lauande, Geraldo Braz Júnior, Marcus Vinicius Oliveira, Gabriel Costa, Matheus Levy, Anselmo Cardoso de Paiva, and João D. Sousa de Almeida. "Aplicando MultiInstance Learning (MIL) para o Diagnóstico de Câncer de Mama em Imagens Histopatológicas." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbcas.2022.222673.
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O câncer de mama é um dos cânceres mais comuns entre as mulheres pelo fato de este contabilizar 29,07% dos casos. Entre todas as variações da doença, o câncer de mama é o mais frequente no Brasil. E por esse motivo é imperativo que sejam desenvolvidas técnicas que agilizem o processo detecção destes tumores para diminuir a taxa de casos terminais. O aprendizado profundo tem se tornando um forte aliado dos patologistas na análise de imagens histopatológicas tomando decisão de maneira rápida e confiável. Neste trabalhos apresentamos uma abordagem baseada em MIL - Multi Instance Learning que tem uma abordagem diferente da tradicional devido ao fato de trabalha com varias instância de uma mesma imagem. Utilizamos para avaliar esse método o dataset de câncer de mama BreakHis. Nos experimentos realizados, foram alçados uma acurácia de 90% e 98% de sensibilidade para classificação binaria (Benigno ou Maligno).
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Santos, Marta C., Ana I. Borges, Davide R. Carneiro, and Flora J. Ferreira. "Synthetic dataset to study breaks in the consumer’s water consumption patterns." In ICoMS 2021: 2021 4th International Conference on Mathematics and Statistics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3475827.3475836.
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Pal, S., C. Iek, L. J. Peltier, A. Smirnov, K. J. Knight, D. Zheng, and J. Jarvis. "Verification and Validation of CFD Model to Predict Jet Loads and Blast Wave Pressures From High Pressure Superheated Steam Line Break." In ASME 2016 Power Conference collocated with the ASME 2016 10th International Conference on Energy Sustainability and the ASME 2016 14th International Conference on Fuel Cell Science, Engineering and Technology. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/power2016-59675.
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High pressure superheated or saturated steam line breaks in a nuclear power plant generate high speed jet flows and blast waves. The jet loads and blast wave pressures can damage critical nuclear power plant components. An accurate assessment of these effects including uncertainty quantification (UQ), is essential to confirm that design is robust enough to handle jet flows and blast waves from postulated steam line breaks. This paper presents the verification and validation of a computational model created using a commercial CFD code for making such assessments. The verification and validation process involves the steps of application space parametrization, Phenomena Identification and Ranking (PIR), CFD model lockdown, selection of validation dataset, and calculation of formal validation metrics. The Uncertainty Quantification in the actual application should include the propagated validation uncertainties from the validation test problems.
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Lamb, Nikolas, Cameron Palmer, Benjamin Molloy, Sean Banerjee, and Natasha Kholgade Banerjee. "Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2023. http://dx.doi.org/10.1109/cvpr52729.2023.00454.
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Pazi, Idan, Dvir Ginzburg, and Dan Raviv. "Unsupervised Scale-Invariant Multispectral Shape Matching." In 24th Irish Machine Vision and Image Processing Conference. Irish Pattern Recognition and Classification Society, 2022. http://dx.doi.org/10.56541/vhmq4826.
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Alignment between non-rigid stretchable structures is one of the most challenging tasks in computer vision, as the invariant properties are hard to define, and there is no labeled data for real datasets. We present unsupervised neural network architecture based upon the spectral domain of scale-invariant geometry. We build on top of the functional maps architecture, but show that learning local features, as done until now, is not enough once the isometry assumption breaks. We demonstrate the use of multiple scale-invariant geometries for solving this problem. Our method is agnostic to local-scale deformations and shows superior performance for matching shapes from different domains when compared to existing spectral state-of-the-art solutions.
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Han, Jiyeon, Kyowoon Lee, Anh Tong, and Jaesik Choi. "Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/340.
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In the analysis of sequential data, the detection of abrupt changes is important in predicting future events. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified thresholds for the tests, and use them to improve Bayesian Online Change Point Detection (BOCPD) by confirming statistically significant changes and non-changes. Our Confirmatory BOCPD (CBOCPD) algorithm finds multiple structural breaks in GPs even when hyperparameters are not tuned precisely. We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD. Experimental results on synthetic and real-world datasets show that our proposed algorithm outperforms existing methods for the prediction of nonstationarity in terms of both regression error and log-likelihood.
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Tolstaya, E., A. Shakirov, and M. Mezghani. "Lithology Prediction from Drill Cutting Images Using Convolutional Neural Networks and Automated Dataset Cleaning." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216418-ms.
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Abstract The task of automating the detection of lithology in drill cuttings is an essential aspect of reservoir engineering. As a wellbore is drilled, the rotating bit breaks down the rocks at the bottom of the hole, and these fragments are then transported to the surface through the drilling mud. These fragments are separated from the mud by a shaker upon reaching the surface, enabling the mud to be recycled. The leftover rock fragments, known as drill cuttings, can provide a wealth of information about the geology of the wellbore, drilling speed, and the oil and gas content in the rocks. Once the cuttings are cleaned of the drilling mud, their various properties can be examined using a range of techniques. The importance of automating this analysis process lies in its ability to rapidly evaluate the drilling procedure and predict possible emergencies. Moreover, an accurate analysis of the geological properties of the drill cuttings can provide real-time data similar to well logging. In the domain of oil and gas exploration and drilling, lithology identification can be achieved using a variety of data samples. These include well log data (such as acoustic logs, resistivity logs, gamma-ray logs, and spectral gamma-ray logs) [1,2], laboratory data (such as core samples, X-ray diffraction (XRD), X-ray fluorescence (XRF), and Near-infrared (NIR) spectroscopy, scanning electron microscopy (SEM) [3, 4]), and data collected during drilling (like white light or UV images, images captured under fluorescent light, or real-time drilling parameters such as weight on bit, torque, rate of penetration, mud properties, etc.), but such data could have some bias, a "depth shift", which makes inaccurate correspondence between images from recorded depth, and depth of drilling data ([5]). Acquiring well log or laboratory data tends to be very costly, whereas data collected during drilling is relatively inexpensive. Drill cuttings contain a lot of information as they cover a wider stratigraphic range compared to cores. Analyzing drill cuttings nearly in real-time is a cost-effective approach to characterizing reservoirs, which includes evaluations of mineralogy, petrophysical properties, and mechanical properties. Drill cuttings are especially advantageous due to the cost-effectiveness of obtaining them and the comprehensive depth of the stratigraphic section they represent. Consequently, automating on-site lithology detection based on data collected during drilling is highly desirable. There are several methods to prepare and analyze drill cutting samples, such as using whole cuttings (unprocessed), creating thin sections (where rock samples are ground down to a specific thickness, usually around 30 microns), or making polished sections (which involves preparing a flat, smooth surface on a rock sample). However, the latter two methods necessitate laboratory equipment, making them impractical for field use. In our study, we explore the feasibility of detecting lithology solely through images taken under white light, which represents the most economical data acquisition method during drilling. In our research, we propose a method to determine the lithological composition of drill cuttings by utilizing digital photographs. This method is based on high-resolution images of cuttings taken from specific depths and historical lithological data from previously drilled and examined wells. We created a deep learning model, more specifically a convolutional model, that can predict the probability of a sample being classified into a particular rock category. This model was trained using wells with known lithological data, which were crucial for testing and verifying the model's effectiveness. Looking ahead, we anticipate that this machine learning model will be able to predict the lithological composition of a sample from cleaned cuttings images with a certain level of probability.
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Li, Boyang, Yurong Cheng, Ye Yuan, Guoren Wang, and Lei Chen. "Simultaneous Arrival Matching for New Spatial Crowdsourcing Platforms." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/178.
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In recent years, 3D spatial crowdsourcing platforms become popular, in which users and workers travel together to their assigned workplaces for services, such as InterestingSport and Nanguache. A typical problem over 3D spatial crowdsourcing platforms is to match users with suitable workers and workplaces. Existing studies all ignored that the workers and users assigned to the same workplace should arrive almost at the same time, which is very practical in the real world. Thus, in this paper, we propose a new Simultaneous Arrival Matching (SAM), which enables workers and users to arrive at their assigned workplace within a given tolerant time. We find that the new considered arriving time constraint breaks the monotonic additivity of the result set. Thus, it brings a large challenge in designing effective and efficient algorithms for the SAM. We design Sliding Window algorithm and Threshold Scanning algorithm to solve the SAM. We conduct the experiments on real and synthetic datasets, experimental results show the effectiveness and efficiency of our algorithms.