Yinyin Yuan
Institute of Cancer Research
116 Papers
447 Citations
Yinyin Yuan is an academic researcher from Institute of Cancer Research. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 28, co-authored 80 publications. Previous affiliations of Yinyin Yuan include University of Cambridge & The Royal Marsden NHS Foundation Trust.
Chat about Author
Papers
•Posted Content
Deconvolving convolution neural network for cell detection
Shan E Ahmed Raza,Khalid AbdulJabbar,Mariam Jamal-Hanjani,Selvaraju Veeriah,John Le Quesne,Charles Swanton,Yinyin Yuan +6 more
TL;DR: In this article, the ground truth points are convolved with a mapping filter to generate artifical labels and the output of the trained CNN is then deconvolved to generate points as cell detection.
2
Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns
Xiaoxi Pan,Hanyun Zhang,Anca-Ioana Grapa,Khalid AbdulJabbar,Shan E Ahmed Raza,Ho Kwan Alvin Cheung,Takahiro Karasaki,John Le Quesne,David D. Moore,Charles Swanton,Yinyin Yuan +10 more
TL;DR: In this article , a cross-stream interaction (CroSIn) model is proposed for lung adenocarcinoma growth segmentation, which fully considers crucial interactions across scales to gather abundant information.
2
A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes
TL;DR: This work proposes an integrative approach to learn a sparse interaction network of DNA copy-number regions with their downstream targets in a breast cancer dataset and delineates cis- and trans-effects in a Breast cancer dataset, for which important oncogenes such as ESR1 and ERBB2 appear to be highly copy- number dependent.
2
•Posted Content
ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images
TL;DR: In this paper, the authors proposed a deep learning method to detect and classify cells in mIHC whole-tumour slide images of breast cancer, which integrates conventional dice overlap and a new cell count loss function for optimizing cell detection, followed by a multi-stage convolutional neural network for cell classification.
1
AI-powered pan-species computational pathology: bridging clinic and wildlife care
Khalid AbdulJabbar,Simón P. Castillo,Katherine Hughes,Hannah Davidson,Amy M. Boddy,Lisa M. Abegglen,Elizabeth P. Murchison,Trevor A. Graham,Simon Spiro,Chiara Palmieri,Yinyin Yuan +10 more
TL;DR: This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on a new understanding of morphological conservation, which could vastly accelerate new developments in veterinary medicine and comparative oncology.
1