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10.1109/TNNLS.2021.3099165 http://scihub22266oqcxt.onion/10.1109/TNNLS.2021.3099165 34370672!ä!34370672 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Neural+Netw+Learn+Syst 2021 ; 32 (9): 3786-3797 Nephropedia Template TP {{tp|p=34370672|t=2021. Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning |pdf=|usr=}}{{34370672}} gab.com Text Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning JO: IEEE Trans Neural Netw Learn Syst http://www.kidney.de/pget.php?&tw=1&pmid=34370672 #FOAvip Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/. Twit Text FOAVip Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning ????IEEE Trans Neural Netw Learn Syst http://www.kidney.de/pget.php?&tw=1&pmid=34370672 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34370672 #FOAvip English Wikipedia <ref>{{Cite pmid|34370672}}</ref> Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning #MMPMID34370672 Liu G; Liao Y; Wang F; Zhang B; Zhang L; Liang X; Wan X; Li S; Li Z; Zhang S; Cui S IEEE Trans Neural Netw Learn Syst 2021[Sep]; 32 (9): 3786-3797 PMID34370672show ga Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/. |*Machine Learning[MESH] |Algorithms[MESH] |Artificial Intelligence[MESH] |COVID-19/*diagnostic imaging[MESH] |China[MESH] |Humans[MESH] |Image Interpretation, Computer-Assisted[MESH] |Research Report/*standards[MESH] |Terminology as Topic[MESH] |Tomography, X-Ray Computed[MESH] |Transfer, Psychology[MESH]Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Ge(epmc).pdf DeepDyve Pubget Overpricing