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863 results on '"Patel, Premal"'

10. Fostering research in pediatric interventional radiology: needs assessment and suggestions for support

Author

Temple, Michael J., Abruzzo, Todd A., Muñoz, Fernando Gómez, Amaral, João G., Bogan, Kristi A., Gibson, Craig, Patel, Premal A., Toh, Luke M., Zhang, Jin, Mubarak, Walid M., Connolly, Bairbre L., Mitchell, Sally E., Barnacle, Alex M., Cahill, Anne Marie, Braswell, Leah E., Marshalleck, Francis E., Patel, Manish N., Feola, G. Peter, Chaudry, Gulraiz A., Chennapragada, S. Murthy, Josephs, Shellie C., Annam, Aparna, and Goman, Simal K.

Published
2023
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13. Reply by Authors

Author

Bal, Dhiraj S., Fidel, Maximilian, Shah, Jainik, Urichuk, Matthew, Sidhom, Karim, Pandian, Alagarsamy, Bard, Robert, and Patel, Premal

Published
2024
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19. Heart Disease Diagnosis Detection Based on Chest Pain Using Machine Learning Stacking Classifier

Author

Shah, Jalpa, Shah, Chintan, Patel, Premal, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Fong, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2023
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20. Lung Cancer Prediction Using Machine Learning Models

Author

Singh, Manish, Shah, Chintan, Patel, Premal, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tuba, Milan, editor, Akashe, Shyam, editor, and Joshi, Amit, editor

Published
2023
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21. Liver Diseases’ Stage Detection Using Hybrid Technique of Supervised Machine Learning Classifiers

Author

Patel, Sagar, Shah, Chintan, Patel, Premal, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tuba, Milan, editor, Akashe, Shyam, editor, and Joshi, Amit, editor

Published
2023
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22. Diagnosis of Denizen Cirrhosis Disorders Using Supervised Machine Learning Classifiers

Author

Patel, Sagar, Shah, Chintan, Patel, Premal, Rathod, Dushyantsinh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2023
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23. Canadian Urological Association guideline: Erectile dysfunction.

Author

Domes, Trustin, Najafabadi, Borna, Roberts, Matthew, Campbell, Jeffrey, Flannigan, Ryan, Bach, Phil, Patel, Premal, Langille, Gavin, Krakowsky, Yonah, and Violette, Philippe

Abstract

Erectile dysfunction (ED) impacts the wellness and quality of life of millions of Canadians. An evaluation focused on the identification of reversible and irreversible underlying factors is recommended for patients presenting with ED. Through a shared decision-making model framework, the goal of ED treatment is to improve functional outcomes and enhance sexual satisfaction while minimizing adverse effects associated with treatment. Given that ED is assessed and treated by multiple different types of health practitioners, the purpose of this guideline is to provide the best available evidence to facilitate care delivery through a Canadian lens. After a narrative review of ED assessment and treatment for general readership, five key clinical questions relating to priority areas of ED are assessed using the GRADE and evidence-to-decision-making frameworks.

Published
2021

26. Use of super resolution reconstruction MRI for surgical planning in Placenta accreta spectrum disorder: Case series

Author

Mufti, Nada, Chappell, Joanna, O'Brien, Patrick, Attilakos, George, Irzan, Hassna, Sokolska, Magda, Narayanan, Priya, Gaunt, Trevor, Humphries, Paul D., Patel, Premal, Whitby, Elspeth, Jauniaux, Eric, Hutchinson, J. Ciaran, Sebire, Neil J., Atkinson, David, Kendall, Giles, Ourselin, Sebastien, Vercauteren, Tom, David, Anna L., and Melbourne, Andrew

Published
2023
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28. Comparative assessment of outcomes and adverse effects using two different intramuscular testosterone therapy regimens: 100 mg IM weekly or 200 mg IM biweekly

Author

El-Khatib, Farouk M., Huynh, Linda M., Kopelevich, Alexei, Osman, Mohamad M., Choi, Edward, Nguyen, Jeanie T., Dianatnejad, Sharmin, Wu, Qiaqia, Olivas, Madeline G., Spitz, Aaron, Lowry, Jacob, Losso, Boriss Y., Khera, Mohit, Angulo-Llanos, Laura, Patel, Premal, Ramasamy, Ranjith, and Yafi, Faysal A.

Published
2022
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35. CARDIOVASCULAR OUTCOMES IMPROVE IN CHILDREN WITH RENOVASCULAR HYPERTENSION FOLLOWING ENDOVASCULAR AND SURGICAL INTERVENTIONS

Author

Paessler, Alicia, primary, Redhead, Emily, additional, Arslan, Zainab, additional, Patel, Premal, additional, Minhas, Kishore, additional, Forman, Colin, additional, Hollis, Paolo, additional, Lava, Sebastiano, additional, Ionescu, Florin, additional, Manuel, Devi, additional, Ray, Samiran, additional, Kessaris, Nicos, additional, Giardini, Alessandro, additional, Ratnamma, Vineetha, additional, Dobby, Nadine, additional, Tullus, Kjell, additional, Simmonds, Jacob, additional, and Stojanovic, Jelena, additional

Published
2024
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42. A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography

Author

Banerjee, Jyotirmoy, Patel, Premal A., Ushakov, Fred, Peebles, Donald, Deprest, Jan, Ourselin, Sebastien, Hawkes, David, and Vercauteren, Tom

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a spatial compounding technique and variational framework to improve 3D ultrasound image quality by compositing multiple ultrasound volumes acquired from different probe orientations. In the composite volume, instead of intensity values, we estimate a tensor at every voxel. The resultant tensor image encapsulates the directional information of the underlying imaging data and can be used to generate ultrasound volumes from arbitrary, potentially unseen, probe positions. Extending the work of Hennersperger et al., we introduce a log-Euclidean framework to ensure that the tensors are positive-definite, eventually ensuring non-negative images. Additionally, we regularise the underpinning ill-posed variational problem while preserving edge information by relying on a total variation penalisation of the tensor field in the log domain. We present results on in vivo human data to show the efficacy of the approach., Comment: SPIE Medical Imaging 2018

Published
2018
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46. Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Author

Wang, Guotai, Li, Wenqi, Zuluaga, Maria A., Pratt, Rosalind, Patel, Premal A., Aertsen, Michael, Doel, Tom, David, Anna L., Deprest, Jan, Ourselin, Sebastien, and Vercauteren, Tom

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. To address these problems, we propose a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine-tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine-tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal MR slices, where only two types of these organs were annotated for training; and 3D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only tumor cores in one MR sequence were annotated for training. Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods., Comment: 11 pages, 11 figures

Published
2017
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47. DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

Author

Wang, Guotai, Zuluaga, Maria A., Li, Wenqi, Pratt, Rosalind, Patel, Premal A., Aertsen, Michael, Doel, Tom, David, Anna L., Deprest, Jan, Ourselin, Sebastien, and Vercauteren, Tom

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy. We use one CNN to obtain an initial automatic segmentation, on which user interactions are added to indicate mis-segmentations. Another CNN takes as input the user interactions with the initial segmentation and gives a refined result. We propose to combine user interactions with CNNs through geodesic distance transforms, and propose a resolution-preserving network that gives a better dense prediction. In addition, we integrate user interactions as hard constraints into a back-propagatable Conditional Random Field. We validated the proposed framework in the context of 2D placenta segmentation from fetal MRI and 3D brain tumor segmentation from FLAIR images. Experimental results show our method achieves a large improvement from automatic CNNs, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods., Comment: 14 pages, 15 figures

Published
2017
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