114 related articles for article (PubMed ID: 36085607)
21. Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches.
Zhou X
Adv Exp Med Biol; 2020; 1213():135-147. PubMed ID: 32030668
[TBL] [Abstract][Full Text] [Related]
22. CAM-Wnet: An effective solution for accurate pulmonary embolism segmentation.
Liu Z; Yuan H; Wang H
Med Phys; 2022 Aug; 49(8):5294-5303. PubMed ID: 35609213
[TBL] [Abstract][Full Text] [Related]
23. Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging.
He K; Liu X; Li M; Li X; Yang H; Zhang H
BMC Med Imaging; 2020 Jun; 20(1):59. PubMed ID: 32487083
[TBL] [Abstract][Full Text] [Related]
24. A fully automated rib fracture detection system on chest CT images and its impact on radiologist performance.
Meng XH; Wu DJ; Wang Z; Ma XL; Dong XM; Liu AE; Chen L
Skeletal Radiol; 2021 Sep; 50(9):1821-1828. PubMed ID: 33599801
[TBL] [Abstract][Full Text] [Related]
25. Lymph Node Size on Computed Tomography Images Is a Predictive Indicator for Lymph Node Metastasis in Patients with Colorectal Neuroendocrine Tumors.
Tanaka T; Nozawa H; Kawai K; Hata K; Kiyomatsu T; Nishikawa T; Otani K; Sasaki K; Murono K; Watanabe T
In Vivo; 2017; 31(5):1011-1017. PubMed ID: 28882974
[TBL] [Abstract][Full Text] [Related]
26. CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma.
Yang M; He X; Xu L; Liu M; Deng J; Cheng X; Wei Y; Li Q; Wan S; Zhang F; Wu L; Wang X; Song B; Liu M
Front Oncol; 2022; 12():961779. PubMed ID: 36249050
[TBL] [Abstract][Full Text] [Related]
27. TransEffiDet: Aircraft Detection and Classification in Aerial Images Based on EfficientDet and Transformer.
Wang Y; Wang T; Zhou X; Cai W; Liu R; Huang M; Jing T; Lin M; He H; Wang W; Zhu Y
Comput Intell Neurosci; 2022; 2022():2262549. PubMed ID: 35498209
[TBL] [Abstract][Full Text] [Related]
28. Role of the standardized uptake value of 18-fluorodeoxyglucose positron emission tomography-computed tomography in detecting the primary tumor and lymph node metastasis in colorectal cancers.
Uchiyama S; Haruyama Y; Asada T; Hotokezaka M; Nagamachi S; Chijiiwa K
Surg Today; 2012 Oct; 42(10):956-61. PubMed ID: 22711186
[TBL] [Abstract][Full Text] [Related]
29. Sensitivity of PET/MR images in liver metastases from colorectal carcinoma.
Yong TW; Yuan ZZ; Jun Z; Lin Z; He WZ; Juanqi Z
Hell J Nucl Med; 2011; 14(3):264-8. PubMed ID: 22087447
[TBL] [Abstract][Full Text] [Related]
30. Positron emission tomography/computerized tomography functional imaging of esophageal and colorectal cancer.
Larson SM; Schoder H; Yeung H
Cancer J; 2004; 10(4):243-50. PubMed ID: 15383205
[TBL] [Abstract][Full Text] [Related]
31. Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images.
Li L; Wei M; Liu B; Atchaneeyasakul K; Zhou F; Pan Z; Kumar SA; Zhang JY; Pu Y; Liebeskind DS; Scalzo F
IEEE J Biomed Health Inform; 2021 May; 25(5):1646-1659. PubMed ID: 33001810
[TBL] [Abstract][Full Text] [Related]
32. Role of PET/CT in the detection of liver metastases from colorectal cancer.
Orlacchio A; Schillaci O; Fusco N; Broccoli P; Maurici M; Yamgoue M; Danieli R; D'Urso S; Simonetti G
Radiol Med; 2009 Jun; 114(4):571-85. PubMed ID: 19444590
[TBL] [Abstract][Full Text] [Related]
33. [Evaluation of positron emission tomography by using F-18-fluorodeoxyglucose in diagnosis of recurrent colorectal cancer].
Kula Z; Szefer J; Zuchora Z; Romanowicz G; Pietrzak T
Pol Merkur Lekarski; 2004; 17 Suppl 1():63-6. PubMed ID: 15603351
[TBL] [Abstract][Full Text] [Related]
34. Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.
Fung DLX; Liu Q; Zammit J; Leung CK; Hu P
J Transl Med; 2021 Jul; 19(1):318. PubMed ID: 34311742
[TBL] [Abstract][Full Text] [Related]
35. The value of FDG positron emission tomography/computerised tomography (PET/CT) in pre-operative staging of colorectal cancer: a systematic review and economic evaluation.
Brush J; Boyd K; Chappell F; Crawford F; Dozier M; Fenwick E; Glanville J; McIntosh H; Renehan A; Weller D; Dunlop M
Health Technol Assess; 2011 Sep; 15(35):1-192, iii-iv. PubMed ID: 21958472
[TBL] [Abstract][Full Text] [Related]
36. Prospective study on diagnostic and prognostic significance of postoperative FDG PET/CT in recurrent colorectal carcinoma patients: comparison with MRI and tumor markers.
Odalovic S; Stojiljkovic M; Sobic-Saranovic D; Pandurevic S; Brajkovic L; Milosevic I; Grozdic-Milojevic I; Artiko V
Neoplasma; 2017; 64(6):954-961. PubMed ID: 28895416
[TBL] [Abstract][Full Text] [Related]
37. Contrast-Enhanced CT-based Textural Parameters as Potential Prognostic Factors of Survival for Colorectal Cancer Patients Receiving Targeted Therapy.
Zhao Y; Yang J; Luo M; Yang Y; Guo X; Zhang T; Hao J; Yao Y; Ma X
Mol Imaging Biol; 2021 Jun; 23(3):427-435. PubMed ID: 33108800
[TBL] [Abstract][Full Text] [Related]
38. A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.
Gong H; Yu L; Leng S; Dilger SK; Ren L; Zhou W; Fletcher JG; McCollough CH
Med Phys; 2019 May; 46(5):2052-2063. PubMed ID: 30889282
[TBL] [Abstract][Full Text] [Related]
39. U-Net: A valuable encoder-decoder architecture for liver tumors segmentation in CT images.
Sahli H; Ben Slama A; Labidi S
J Xray Sci Technol; 2022; 30(1):45-56. PubMed ID: 34806644
[TBL] [Abstract][Full Text] [Related]
40. Use of laxative-augmented contrast medium in the evaluation of colorectal foci at FDG PET.
Chen YK; Chen JH; Tsui CC; Chou HH; Cheng RH; Chiu JS
Radiology; 2011 May; 259(2):525-33. PubMed ID: 21406631
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]