Robert S. Mannel

发表

Assessing the performance of quantitative image features on early stage prediction of treatment effectiveness for ovary cancer patients: a preliminary investigation

Bin Zheng, Theresa C. Thai, Kathleen N. Moore, 2018, BiOS.

Utilizing deep learning technology to develop a novel CT image marker for categorizing cervical cancer patients at early stage

Hong Liu, Bin Zheng, Theresa C. Thai, 2019, BiOS.

Improving efficacy of metastatic tumor segmentation to facilitate early prediction of ovarian cancer patients' response to chemotherapy

Bin Zheng, Gopichandh Danala, Theresa C. Thai, 2017, BiOS.

Developing a low cost image marker to identify lymph node metastasis for cervical cancer patients: an initial study

Bin Zheng, Hong Liu, Theresa C. Thai, 2020, BiOS.

Development of a transferring GAN based CAD scheme for breast mass classification: an initial study

Bin Zheng, Theresa C. Thai, Yuchen Qiu, 2021, BiOS.

Recent advances and clinical applications of deep learning in medical image analysis pdf

Bin Zheng, Theresa C. Thai, Yuchen Qiu, 2021, ArXiv.

Virtual adversarial training for semi-supervised breast mass classification

Theresa C. Thai, Robert S. Mannel, Xuxin Chen, 2022, BiOS.

Impact of Individual Physicians on Enrollment of Patients into Clinical Trials

Michael A. Gold, Robert S. Mannel, Scott D. McMeekin, 2003, American journal of clinical oncology.

Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.

Bin Zheng, Gopichandh Danala, Yuchen Qiu, 2017, Academic radiology.

Incorporation of bevacizumab in the primary treatment of ovarian cancer

Jeffrey M. Fowler, Michael J. Birrer, Robert S. Mannel, 2012 .