File:Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models.pdf
Size of this JPG preview of this PDF file: 463 × 599 pixels. Other resolutions: 185 × 240 pixels | 371 × 480 pixels | 593 × 768 pixels | 1,275 × 1,650 pixels.
Original file (1,275 × 1,650 pixels, file size: 1.25 MB, MIME type: application/pdf, 9 pages)
File information
Structured data
Captions
Summary
editDescriptionGenerating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models.pdf |
English: Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality. Aiming to counter this issue, this research draws on the advancements in machine learning techniques in other fields for the generation of artificial data. The generated methods were tested through the evaluation of their output against that of a real-world labelled MRS brain tumor data-set. Furthermore the resultant output from the generative techniques were each used to train separate traditional classifiers which were tested on a subset of the real MRS brain tumor dataset. The results suggest that there exist methods capable of producing accurate, ground truth based MRS voxels. These findings indicate that through generative techniques, large datasets can be made available for training deep, learning models for the use in brain tumor diagnosis. |
Date | |
Source | Content available at arXiv.org (Dedicated link) (archive.org link) |
Author | Nathan J Olliverre, Guang Yang, Gregory Slabaugh, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso |
Licensing
editThis file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.
- You are free:
- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
File history
Click on a date/time to view the file as it appeared at that time.
Date/Time | Thumbnail | Dimensions | User | Comment | |
---|---|---|---|---|---|
current | 02:22, 9 November 2018 | 1,275 × 1,650, 9 pages (1.25 MB) | Acagastya (talk | contribs) | User created page with UploadWizard |
You cannot overwrite this file.
File usage on Commons
There are no pages that use this file.
Metadata
This file contains additional information such as Exif metadata which may have been added by the digital camera, scanner, or software program used to create or digitize it. If the file has been modified from its original state, some details such as the timestamp may not fully reflect those of the original file. The timestamp is only as accurate as the clock in the camera, and it may be completely wrong.
Short title | |
---|---|
Image title | |
Author | |
Software used | LaTeX with hyperref package |
Conversion program | pdfTeX-1.40.17 |
Encrypted | no |
Page size | 612 x 792 pts (letter) |
Version of PDF format | 1.5 |