1 code implementation • 1 Nov 2023 • Xingru Huang, Yihao Guo, Jian Huang, Zhi Li, Tianyun Zhang, Kunyan Cai, Gaopeng Huang, WenHao Chen, Zhaoyang Xu, Liangqiong Qu, Ji Hu, Tinyu Wang, Shaowei Jiang, Chenggang Yan, Yaoqi Sun, Xin Ye, Yaqi Wang
Macular hole diagnosis and treatment rely heavily on spatial and quantitative data, yet the scarcity of such data has impeded the progress of deep learning techniques for effective segmentation and real-time 3D reconstruction.
no code implementations • 9 May 2023 • Guangliang Cheng, Yunmeng Huang, Xiangtai Li, Shuchang Lyu, Zhaoyang Xu, Qi Zhao, Shiming Xiang
We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and learning frameworks in the methodology section.
no code implementations • 17 Nov 2020 • Ellery Wulczyn, David F. Steiner, Melissa Moran, Markus Plass, Robert Reihs, Fraser Tan, Isabelle Flament-Auvigne, Trissia Brown, Peter Regitnig, Po-Hsuan Cameron Chen, Narayan Hegde, Apaar Sadhwani, Robert MacDonald, Benny Ayalew, Greg S. Corrado, Lily H. Peng, Daniel Tse, Heimo Müller, Zhaoyang Xu, Yun Liu, Martin C. Stumpe, Kurt Zatloukal, Craig H. Mermel
Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.
no code implementations • 16 Dec 2019 • Ellery Wulczyn, David F. Steiner, Zhaoyang Xu, Apaar Sadhwani, Hongwu Wang, Isabelle Flament, Craig H. Mermel, Po-Hsuan Cameron Chen, Yun Liu, Martin C. Stumpe
Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages.
no code implementations • 31 Jan 2019 • Zhaoyang Xu, Faranak Sobhani, Carlos Fernandez Moro, Qianni Zhang
We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images.
no code implementations • 13 Jan 2019 • Zhaoyang Xu, Xingru Huang, Carlos Fernández Moro, Béla Bozóky, Qianni Zhang
We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide.