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3D parametric model 1

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hilar cholangiocarcinoma 1

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primary sclerosing cholangitis 1

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Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

Frontiers of Medicine 2020, Volume 14, Issue 4,   Pages 470-487 doi: 10.1007/s11684-020-0782-9

Abstract: deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks

Keywords: pathology     deep learning     segmentation     detection     classification    

Hilar cholangiocarcinoma: Pathology and tumor biology

Dong KUANG, Guo-Ping WANG,

Frontiers of Medicine 2010, Volume 4, Issue 4,   Pages 371-377 doi: 10.1007/s11684-010-0130-6

Abstract: Hilar cholangiocarcinoma, first described by Klatskin in 1965, is a relatively rare tumor arising from the bile ducts. The histomorphological features of hilar cholangiocarcinoma are identical with other extra- and intra-hepatic bile duct carcinomas. The most common disease associated with cholangiocarcinoma is primary sclerosing cholangitis. The development of cholangiocarcinoma is a multistep process associated with several mutations in oncogenes and tumor-suppressor genes. Based on macroscopic appearance, three distinct subtypes have been described: sclerosing, nodular, and papillary. Microscopically, more than 95% of tumors are adenocarcinomas. Hilar cholangiocarcinoma is a slowly growing tumor and tends to spread longitudinally along the bile ducts with neural, perineural, and subepithelial extension. Lymph node invasion can be found in 30%–50% patients at the time of diagnosis, but blood-born metastases are rare and usually occur at late stages.

Keywords: hilar cholangiocarcinoma     morphology     primary sclerosing cholangitis     metastasis     growth    

Coronavirus disease 2019 (COVID-19): a clinical update

Min Zhou, Xinxin Zhang, Jieming Qu

Frontiers of Medicine 2020, Volume 14, Issue 2,   Pages 126-135 doi: 10.1007/s11684-020-0767-8

Abstract: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has posed a significant threat to global health. It caused a total of 80 868 confirmed cases and 3101 deaths in Chinese mainland until March 8, 2020. This novel virus spread mainly through respiratory droplets and close contact. As disease progressed, a series of complications tend to develop, especially in critically ill patients. Pathological findings showed representative features of acute respiratory distress syndrome and involvement of multiple organs. Apart from supportive care, no specific treatment has been established for COVID-19. The efficacy of some promising antivirals, convalescent plasma transfusion, and tocilizumab needs to be investigated by ongoing clinical trials.

Keywords: coronavirus disease 2019     epidemiology     pathology     radiology     clinical characteristics     treatment    

Visual recognition of cardiac pathology based on 3D parametric model reconstruction Research Article

Jinxiao XIAO, Yansong LI, Yun TIAN, Dongrong XU, Penghui LI, Shifeng ZHAO, Yunhe PAN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9,   Pages 1324-1337 doi: 10.1631/FITEE.2200102

Abstract: A new method for classification of cardiac pathology is therefore proposed based on reconstruction.Finally, shape and motion features of 3D heart models across two phases are extracted to classify cardiac pathology

Keywords: 3D visual knowledge     3D parametric model     Cardiac pathology diagnosis     Data augmentation    

Title Author Date Type Operation

Deep learning in digital pathology image analysis: a survey

Shujian Deng, Xin Zhang, Wen Yan, Eric I-Chao Chang, Yubo Fan, Maode Lai, Yan Xu

Journal Article

Hilar cholangiocarcinoma: Pathology and tumor biology

Dong KUANG, Guo-Ping WANG,

Journal Article

Coronavirus disease 2019 (COVID-19): a clinical update

Min Zhou, Xinxin Zhang, Jieming Qu

Journal Article

Visual recognition of cardiac pathology based on 3D parametric model reconstruction

Jinxiao XIAO, Yansong LI, Yun TIAN, Dongrong XU, Penghui LI, Shifeng ZHAO, Yunhe PAN

Journal Article