What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Cancer
- Artificial intelligence
His primary scientific interests are in Neuroscience, Resting state fMRI, Artificial intelligence, Brain mapping and Internal medicine.
He combines topics linked to Acupuncture with his work on Neuroscience.
His work deals with themes such as Aura and Anterior cingulate cortex, Psychiatry, Cognition, Stroop effect, which intersect with Resting state fMRI.
His Artificial intelligence research integrates issues from Computer vision and Pattern recognition.
His Brain mapping study integrates concerns from other disciplines, such as Insula, Functional imaging and Audiology.
Jie Tian works mostly in the field of Internal medicine, limiting it down to topics relating to Oncology and, in certain cases, Cohort and Radiomics.
His most cited work include:
- Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer (630 citations)
- Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer. (304 citations)
- DNA Origami as an In Vivo Drug Delivery Vehicle for Cancer Therapy (292 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Radiology, Neuroscience, Tomography and Computer vision.
In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition.
His Radiology research is multidisciplinary, incorporating perspectives in Logistic regression, Nomogram, Cohort and Receiver operating characteristic.
His study on Neuroscience is mostly dedicated to connecting different topics, such as Acupuncture.
The concepts of his Tomography study are interwoven with issues in Algorithm, Inverse problem, Iterative reconstruction and Molecular imaging.
His research integrates issues of Anterior cingulate cortex, Psychiatry, Cognition, Brain activity and meditation and Brain mapping in his study of Resting state fMRI.
He most often published in these fields:
- Artificial intelligence (22.53%)
- Radiology (20.49%)
- Neuroscience (21.32%)
What were the highlights of his more recent work (between 2019-2021)?
- Radiology (20.49%)
- Artificial intelligence (22.53%)
- Receiver operating characteristic (12.12%)
In recent papers he was focusing on the following fields of study:
Radiology, Artificial intelligence, Receiver operating characteristic, Radiomics and Nomogram are his primary areas of study.
His studies in Radiology integrate themes in fields like Logistic regression, Cohort and Confidence interval.
Cohort is a subfield of Internal medicine that Jie Tian tackles.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
His Radiomics research is multidisciplinary, relying on both Hepatocellular carcinoma and Carcinoma.
His Nomogram research incorporates elements of Cancer, Lymph node metastasis, Metastasis, Neuroradiology and Feature.
Between 2019 and 2021, his most popular works were:
- A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. (107 citations)
- A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. (107 citations)
- A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. (107 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Cancer
- Artificial intelligence
Jie Tian mostly deals with Radiology, Nomogram, Receiver operating characteristic, Internal medicine and Radiomics.
His Radiology research is multidisciplinary, incorporating perspectives in Cancer, Neuroradiology, Lymph node and Confidence interval.
Jie Tian combines subjects such as Logistic regression and Cohort with his study of Receiver operating characteristic.
His work in Internal medicine addresses issues such as Oncology, which are connected to fields such as Chemotherapy and Proportional hazards model.
His Radiomics study deals with the bigger picture of Artificial intelligence.
His Artificial intelligence study combines topics in areas such as Automation, Significant difference and Pattern recognition.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
