most citedOn the Generation of Medical Dialogues for COVID-19

10 citations · 14 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG2020

Learning by Ignoring, with Application to Domain Adaptation

Xingchen Zhao, Xuehai He, Pengtao Xie

Learning by ignoring, which identifies less important things and excludes them from the learning process, is broadly practiced in human learning and has shown ubiquitous effectiven…

cs.CV2020

Pathological Visual Question Answering

Xuehai He, Zhuo Cai, Wenlan Wei +4

Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology (ABP)? To build such a system, three challenges need to be…

cs.CV20204 cited

Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms

Xingyi Yang, Xuehai He, Yuxiao Liang +3

Pretraining has become a standard technique in computer vision and natural language processing, which usually helps to improve performance substantially. Previously, the most domin…

cs.CL202010 cited

On the Generation of Medical Dialogues for COVID-19

Wenmian Yang, Guangtao Zeng, Bowen Tan +9

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of c…

cs.CL2020

PathVQA: 30000+ Questions for Medical Visual Question Answering

Xuehai He, Yichen Zhang, Luntian Mou +2

Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology? To achieve this goal, the first step is to create a visual…

cs.LG2020

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

Xingyi Yang, Xuehai He, Jinyu Zhao +3

During the outbreak time of COVID-19, computed tomography (CT) is a useful manner for diagnosing COVID-19 patients. Due to privacy issues, publicly available COVID-19 CT datasets a…