132 related articles for article (PubMed ID: 36731328)
1. Bone suppression of lateral chest x-rays with imperfect and limited dual-energy subtraction images.
Liu Y; Zeng F; Ma M; Zheng B; Yun Z; Qin G; Yang W; Feng Q
Comput Med Imaging Graph; 2023 Apr; 105():102186. PubMed ID: 36731328
[TBL] [Abstract][Full Text] [Related]
2. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain.
Yang W; Chen Y; Liu Y; Zhong L; Qin G; Lu Z; Feng Q; Chen W
Med Image Anal; 2017 Jan; 35():421-433. PubMed ID: 27589577
[TBL] [Abstract][Full Text] [Related]
3. High-Resolution Chest X-Ray Bone Suppression Using Unpaired CT Structural Priors.
Li H; Han H; Li Z; Wang L; Wu Z; Lu J; Zhou SK
IEEE Trans Med Imaging; 2020 Oct; 39(10):3053-3063. PubMed ID: 32275586
[TBL] [Abstract][Full Text] [Related]
4. A review on lung boundary detection in chest X-rays.
Candemir S; Antani S
Int J Comput Assist Radiol Surg; 2019 Apr; 14(4):563-576. PubMed ID: 30730032
[TBL] [Abstract][Full Text] [Related]
5. Bone suppression on pediatric chest radiographs via a deep learning-based cascade model.
Cho K; Seo J; Kyung S; Kim M; Hong GS; Kim N
Comput Methods Programs Biomed; 2022 Mar; 215():106627. PubMed ID: 35032722
[TBL] [Abstract][Full Text] [Related]
6. A disentangled generative model for disease decomposition in chest X-rays via normal image synthesis.
Tang Y; Tang Y; Zhu Y; Xiao J; Summers RM
Med Image Anal; 2021 Jan; 67():101839. PubMed ID: 33080508
[TBL] [Abstract][Full Text] [Related]
7. Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose.
Hong GS; Do KH; Son AY; Jo KW; Kim KP; Yun J; Lee CW
Eur Radiol; 2021 Jul; 31(7):5160-5171. PubMed ID: 33439320
[TBL] [Abstract][Full Text] [Related]
8. Separation of bones from soft tissue in chest radiographs: Anatomy-specific orientation-frequency-specific deep neural network convolution.
Zarshenas A; Liu J; Forti P; Suzuki K
Med Phys; 2019 May; 46(5):2232-2242. PubMed ID: 30848498
[TBL] [Abstract][Full Text] [Related]
9. Performance improvement in multi-label thoracic abnormality classification of chest X-rays with noisy labels.
Yang M; Tanaka H; Ishida T
Int J Comput Assist Radiol Surg; 2023 Jan; 18(1):181-189. PubMed ID: 35616775
[TBL] [Abstract][Full Text] [Related]
10. GAN-based disentanglement learning for chest X-ray rib suppression.
Han L; Lyu Y; Peng C; Zhou SK
Med Image Anal; 2022 Apr; 77():102369. PubMed ID: 35091279
[TBL] [Abstract][Full Text] [Related]
11. Deep Mining External Imperfect Data for Chest X-Ray Disease Screening.
Luo L; Yu L; Chen H; Liu Q; Wang X; Xu J; Heng PA
IEEE Trans Med Imaging; 2020 Nov; 39(11):3583-3594. PubMed ID: 32746106
[TBL] [Abstract][Full Text] [Related]
12. DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.
Rajaraman S; Cohen G; Spear L; Folio L; Antani S
PLoS One; 2022; 17(3):e0265691. PubMed ID: 35358235
[TBL] [Abstract][Full Text] [Related]
13. Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images.
Ferreira JR; Armando Cardona Cardenas D; Moreno RA; de Fatima de Sa Rebelo M; Krieger JE; Antonio Gutierrez M
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1238-1241. PubMed ID: 33018211
[TBL] [Abstract][Full Text] [Related]
14. Automatic Localization and Identification of Thoracic Diseases from Chest X-rays with Deep Learning.
Zhang S; Tang T; Peng X; Zhang Y; Yang W; Li W; Xin X; Zhang J; Wang W; Zhang B
Curr Med Imaging; 2022; 18(13):1416-1425. PubMed ID: 35593336
[TBL] [Abstract][Full Text] [Related]
15. Enhancement of chest radiographs obtained in the intensive care unit through bone suppression and consistent processing.
Chen S; Zhong S; Yao L; Shang Y; Suzuki K
Phys Med Biol; 2016 Mar; 61(6):2283-301. PubMed ID: 26930386
[TBL] [Abstract][Full Text] [Related]
16. Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial Network and Dual-Energy Subtraction.
Bae K; Oh DY; Yun ID; Jeon KN
Korean J Radiol; 2022 Jan; 23(1):139-149. PubMed ID: 34983100
[TBL] [Abstract][Full Text] [Related]
17. An Efficient and Robust Method for Chest X-ray Rib Suppression That Improves Pulmonary Abnormality Diagnosis.
Xu D; Xu Q; Nhieu K; Ruan D; Sheng K
Diagnostics (Basel); 2023 May; 13(9):. PubMed ID: 37175044
[TBL] [Abstract][Full Text] [Related]
18. Framework for dual-energy-like chest radiography image synthesis from single-energy computed tomography based on cycle-consistent generative adversarial network.
Lee M; Lee H; Lee D; Cho H; Choi J; Cha BK; Kim K
Med Phys; 2024 Feb; 51(2):1509-1530. PubMed ID: 36846955
[TBL] [Abstract][Full Text] [Related]
19. COVID-19 classification of X-ray images using deep neural networks.
Keidar D; Yaron D; Goldstein E; Shachar Y; Blass A; Charbinsky L; Aharony I; Lifshitz L; Lumelsky D; Neeman Z; Mizrachi M; Hajouj M; Eizenbach N; Sela E; Weiss CS; Levin P; Benjaminov O; Bachar GN; Tamir S; Rapson Y; Suhami D; Atar E; Dror AA; Bogot NR; Grubstein A; Shabshin N; Elyada YM; Eldar YC
Eur Radiol; 2021 Dec; 31(12):9654-9663. PubMed ID: 34052882
[TBL] [Abstract][Full Text] [Related]
20. Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning.
Pedrosa J; Aresta G; Ferreira C; Carvalho C; Silva J; Sousa P; Ribeiro L; Mendonça AM; Campilho A
Sci Rep; 2022 Apr; 12(1):6596. PubMed ID: 35449199
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
