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- Title
Bone Metastasis Detection in the Chest and Pelvis from a Whole-Body Bone Scan Using Deep Learning and a Small Dataset.
- Authors
Cheng, Da-Chuan; Liu, Chia-Chuan; Hsieh, Te-Chun; Yen, Kuo-Yang; Kao, Chia-Hung; Sun, Jian
- Abstract
The aim of this study was to establish an early diagnostic system for the identification of the bone metastasis of prostate cancer in whole-body bone scan images by using a deep convolutional neural network (D-CNN). The developed system exhibited satisfactory performance for a small dataset containing 205 cases, 100 of which were of bone metastasis. The sensitivity and precision for bone metastasis detection and classification in the chest were 0.82 ± 0.08 and 0.70 ± 0.11, respectively. The sensitivity and specificity for bone metastasis classification in the pelvis were 0.87 ± 0.12 and 0.81 ± 0.11, respectively. We propose the use of hard example mining for increasing the sensitivity and precision of the chest D-CNN. The developed system has the potential to provide a prediagnostic report for physicians' final decisions.
- Subjects
BONE metastasis; CONVOLUTIONAL neural networks; PHYSICIANS; DEEP learning; PELVIS; SYSTEM identification
- Publication
Electronics (2079-9292), 2021, Vol 10, Issue 10, p1201
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics10101201