Deep learning in medical imaging: A bit of demystification
For practitioners in the field of medical imaging to gain basic understanding about the technique of deep learning (DL); to understand its basic premise, approach, and implementation. DL is an AI technique where computers are trained to solve a problem by iterating millions of times over a set of tr...
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my.um.eprints.352372022-10-12T01:09:07Z http://eprints.um.edu.my/35237/ Deep learning in medical imaging: A bit of demystification Tan, Li Kuo Wong, Jeannie Hsiu Ding Raja Aman, Raja Rizal Azman R Medicine (General) For practitioners in the field of medical imaging to gain basic understanding about the technique of deep learning (DL); to understand its basic premise, approach, and implementation. DL is an AI technique where computers are trained to solve a problem by iterating millions of times over a set of training examples. If a practitioner can define their problem’s inputs and desired outputs numerically and collect large quantities of example data, then DL is relatively easy to implement and will likely give good results. Conference or Workshop Item PeerReviewed text en http://eprints.um.edu.my/35237/1/Profesor%20Madya%20Dr.%20Jeannie%20Wong%20Hsiu%20Ding_115_KJR_2021_TanLK_AOCR_2021_KJR_abstracts%20%281%29.pdf Tan, Li Kuo and Wong, Jeannie Hsiu Ding and Raja Aman, Raja Rizal Azman Deep learning in medical imaging: A bit of demystification. In: 19th Asian Oceanian Congress of Radiology Incorporating Malaysian Congress of Radiology, 1-4 July 2021, Kuala Lumpur (Virtual). (Submitted) |
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R Medicine (General) Tan, Li Kuo Wong, Jeannie Hsiu Ding Raja Aman, Raja Rizal Azman Deep learning in medical imaging: A bit of demystification |
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For practitioners in the field of medical imaging to gain basic understanding about the technique of deep learning (DL); to understand its basic premise, approach, and implementation. DL is an AI technique where computers are trained to solve a problem by iterating millions of times over a set of training examples. If a practitioner can define their problem’s inputs and desired outputs numerically and collect large quantities of example data, then DL is relatively easy to implement and will likely give good results. |
format |
Conference or Workshop Item |
author |
Tan, Li Kuo Wong, Jeannie Hsiu Ding Raja Aman, Raja Rizal Azman |
author_facet |
Tan, Li Kuo Wong, Jeannie Hsiu Ding Raja Aman, Raja Rizal Azman |
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Tan, Li Kuo |
title |
Deep learning in medical imaging: A bit of demystification |
title_short |
Deep learning in medical imaging: A bit of demystification |
title_full |
Deep learning in medical imaging: A bit of demystification |
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Deep learning in medical imaging: A bit of demystification |
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Deep learning in medical imaging: A bit of demystification |
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deep learning in medical imaging: a bit of demystification |
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http://eprints.um.edu.my/35237/1/Profesor%20Madya%20Dr.%20Jeannie%20Wong%20Hsiu%20Ding_115_KJR_2021_TanLK_AOCR_2021_KJR_abstracts%20%281%29.pdf http://eprints.um.edu.my/35237/ |
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