Published December 2022
| Version v1
Journal article
Deep learning in medical imaging
Creators
- 1. Hamamatsu Photonics K.K., Central Research Laboratory, Hamamatsu, Shizuoka (Japan)
Description
Deep learning has attracted as a strong tool for medical imaging field, and has a superior performance than conventional methods. In this paper, I introduce the overview of a deep learning-based positron emission tomography (PET) imaging, including a supervised PET image enhancement and image reconstruction, a state-of-the-art unsupervised PET imaging, and future directions. (author)
Additional details
Additional titles
- Original title (Japanese)
- 医用画像分野におけるディープラーニング
Publishing Information
- Journal Title
- Hoshasen
- Journal Volume
- 47
- Journal Issue
- 4
- Series
- 雑誌名:放射線
- Journal Page Range
- p. 167-171
- ISSN
- 0285-3604
INIS
- Country of Publication
- Japan
- Country of Input or Organization
- Japan
- INIS RN
- 55005834
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- DATASETS; IMAGE PROCESSING; MACHINE LEARNING; NEURAL NETWORKS; NMR IMAGING; PATIENTS; POSITRON COMPUTED TOMOGRAPHY; RADIATION PROTECTION; RADIOACTIVITY; RADIOPHARMACEUTICALS; THERAPEUTIC DOSES; TIME-OF-FLIGHT METHOD
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTERIZED TOMOGRAPHY; DIAGNOSTIC TECHNIQUES; DOCUMENT TYPES; DOSES; DRUGS; EMISSION COMPUTED TOMOGRAPHY; LABELLED COMPOUNDS; LEARNING; MATERIALS; MATHEMATICAL LOGIC; PROCESSING; RADIOACTIVE MATERIALS; TOMOGRAPHY
Optional Information
- Notes
- 53 refs., 2 figs.