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A performance comparison of low- and high-level features learned by deep convolutional neural networks in epithelium and stroma classification.

Y. Du, R. Zhang, A. Zargari, T. Thai, C. Gunderson, K. Moxley, H. Liu, B. Zheng, and Y. Qiu. Medical Imaging: Digital Pathology, volume 10581 of SPIE Proceedings, page 1058116. SPIE, (2018)

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Yuchen Yuan

Yuchen Han

Yuchen Zheng

Xiuhui Qiu

Wei Qiu

Other publications of authors with the same name

A new application of electrical impedance spectroscopy for measuring glucose metabolism: a phantom study.S. Dhurjaty, Y. Qiu, M. Tan, H. Liu, and B. Zheng. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 9417 of SPIE Proceedings, page 94172K. SPIE, (2015)A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.Y. Wang, Y. Qiu, T. Thai, K. Moore, H. Liu, and B. Zheng. Comput. Methods Programs Biomed., (2017)Developing global image feature analysis models to predict cancer risk and prognosis.B. Zheng, Y. Qiu, F. Aghaei, S. Mirniaharikandehei, M. Heidari, and G. Danala. Vis. Comput. Ind. Biomed. Art, (2019)Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.M. Heidari, S. Mirniaharikandehei, A. Khuzani, G. Danala, Y. Qiu, and B. Zheng. Int. J. Medical Informatics, (2020)Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients.K. Zhang, N. Abdoli, P. Gilley, Y. Sadri, X. Chen, T. Thai, L. Dockery, K. Moore, R. Mannel, and Y. Qiu. CoRR, (2023)Early Rumor Detection Based on Deep Recurrent Q-Learning.W. Wang, Y. Qiu, S. Xuan, and W. Yang. Secur. Commun. Networks, (2021)An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology.Y. Qiu, Y. Wang, S. Yan, M. Tan, S. Cheng, H. Liu, and B. Zheng. Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 978521. SPIE, (2016)Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study.Y. Qiu, S. Yan, M. Tan, S. Cheng, H. Liu, and B. Zheng. Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 978520. SPIE, (2016)An automated approach to improve efficacy in detecting residual malignant cancer cell for facilitating prognostic assessment of leukemia: an initial study.Y. Qiu, X. Lu, M. Tan, S. Li, H. Liu, and B. Zheng. Medical Imaging: Digital Pathology, volume 9420 of SPIE Proceedings, page 94200X. SPIE, (2015)Virtual Adversarial Training for Semi-supervised Breast Mass Classification.X. Chen, X. Wang, K. Zhang, K. Fung, T. Thai, K. Moore, R. Mannel, H. Liu, B. Zheng, and Y. Qiu. CoRR, (2022)

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Disambiguation of "Qiu, Yuchen"

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A performance comparison of low- and high-level features learned by deep convolutional neural networks in epithelium and stroma classification.

Please choose a person to relate this publication to

Yuchen Yuan

Yuchen Han

Yuchen Zheng

Xiuhui Qiu

Wei Qiu

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Qiu, Yuchen"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A performance comparison of low- and high-level features learned by deep convolutional neural networks in epithelium and stroma classification.

Please choose a person to relate this publication to

Yuchen Yuan

Yuchen Han

Yuchen Zheng

Xiuhui Qiu

Wei Qiu

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

A performance comparison of low- and high-level features learned by deep convolutional neural networks in epithelium and stroma classification.