• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 61 matches

• 2022
• 
  Aya Aqeel, Germán Corredor, Vidya Sankar Viswanathan, Chuheng Chen, Mogjan Mokhtari, Pingfu Fu, Joseph E. Willis, Anant Madabhushi:
  Computer extracted features of tumor-infiltrating lymphocytes (TILs) architecture are prognostic of progression-free survival in stage III colon cancer. Digital and Computational Pathology 2022
• 
  Deepak Bajhaiya, Sujatha Narayanan Unni:
  Deep learning-enabled classification of gastric ulcers from wireless capsule endoscopic images. Digital and Computational Pathology 2022
• 
  Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Qi Yang, Xin Yu, Sophie Chiron, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo:
  Inpainting missing tissue in multiplexed immunofluorescence imaging. Digital and Computational Pathology 2022
• 
  Samuel P. Border, Brandon Ginley, John E. Tomaszewski, Pinaki Sarder:
  HistoLens: a generalizable tool for increasing accessibility and interpretability of quantitative analyses in digital pathology. Digital and Computational Pathology 2022
• 
  Michael Brehler, Allison K. Lowman, Samuel Bobholz, Savannah R. Duenweg, Fitzgerald Kyereme, Cassandra Naze, John Sherman, Kenneth A. Iczkowski, Peter S. LaViolette:
  An automated approach for annotating Gleason patterns in whole-mount prostate cancer histology using deep learning. Digital and Computational Pathology 2022
• 
  Jon Camp, Gregory Otteson, Jansen Seheult, Min Shi, Dragan Jevremovic, Horatiu Olteanu, Ahmad Nanaa, Aref Al-Kali, Mohamed Salama, David R. Holmes III:
  Deep neural network for cell type differentiation in myelodysplastic syndrome diagnosis performs similarly when trained on compensated or uncompensated data. Digital and Computational Pathology 2022
• 
  David R. Chambers, Bradley B. Brimhall, Donald R. Poole Jr., Edward A. Medina:
  Cancer cell segmentation for cellularity prediction via a weakly labeled/strongly labeled hybrid convolutional neural network. Digital and Computational Pathology 2022
• 
  Chuheng Chen, Cheng Lu, Joseph E. Willis, Anant Madabhushi:
  Identifying the origination of liver metastasis using a hand-crafted computational pathology approach. Digital and Computational Pathology 2022
• 
  Antong Chen, Tosha Shah, Andrew Brown, Haleh Akrami, Albert Swiston, Amir Vajdi, Radha Krishnan, Razvan Cristescu:
  Prediction of tumor mutation burden from H&E whole-slide images: a comparison of training strategies with convolutional neural networks. Digital and Computational Pathology 2022
• 
  Alison M. Cheung, Dan Wang, Kela Liu, Sarah Hynes, Ben Wang, Simone Stone, Pamela Ohashi, Martin J. Yaffe:
  Spatial analysis of cellular arrangement using quantitative, single-cell imaging of protein multiplexing. Digital and Computational Pathology 2022
• 
  Rakesh Choudhary, Dhadma Balachandran, Jonathan Folmsbee, Jawaria Rahman, Margaret Brandwein-Weber, Scott Doyle:
  Automatic flagging of AI segmentation errors in computational pathology. Digital and Computational Pathology 2022
• 
  Salma Dammak, Matthew J. Cecchini, Aaron D. Ward:
  Using deep learning to predict tumor mutational burden in lung squamous cell carcinoma from 20 centers. Digital and Computational Pathology 2022
• 
  Junwei Deng, Yiqing Shen, Yi Guo, Jing Ke:
  CellSegNet: an adaptive multi-resolution hybrid network for cell segmentation. Digital and Computational Pathology 2022
• 
  Ruining Deng, Haichun Yang, Zuhayr Asad, Zheyu Zhu, Shiru Wang, Lee E. Wheless, Agnes B. Fogo, Yuankai Huo:
  Dense multi-object 3D glomerular reconstruction and quantification on 2D serial section whole slide images. Digital and Computational Pathology 2022
• 
  Farzad Fereidouni, Richard M. Levenson:
  DUET dual-mode scanning of H&E slides reveals novel contrast. Digital and Computational Pathology 2022
• 
  Farzad Fereidouni, Taryn Morningstar, Alexander Borowsky, Richard M. Levenson:
  FIBI: a direct-to-digital microscopy approach for slide-free histology. Digital and Computational Pathology 2022
• 
  Nikolai Fetisov, Lawrence O. Hall, Dmitry B. Goldgof, Matthew B. Schabath:
  Survival time prediction from unannotated lung cancer histopathology images. Digital and Computational Pathology 2022
• 
  Jonathan Folmsbee, Scott Doyle, Rakesh Choudhary, Margaret Brandwein-Weber, Jawaria Rahman:
  Cloud-based platform for human-in-the-loop re-annotation of whole slide imaging: large scale semantic pathology segmentation. Digital and Computational Pathology 2022
• 
  Yabo Fu, Yang Lei, David M. Schuster, Sagar A. Patel, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang:
  Deformable histopathology-MRI image registration using deep learning. Digital and Computational Pathology 2022
• 
  Alvaro Sandino Garzon, Ruchika Verma, Yijang Chen, David Becerra Tovar, Eduardo Romero Castro, Pallavi Tiwari:
  A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology. Digital and Computational Pathology 2022
• 
  Parmida Ghahremani, Arie E. Kaufman:
  CrowdDeep: deep-learning from the crowd for nuclei segmentation. Digital and Computational Pathology 2022
• 
  Darshana Govind, Saber Meamardoost, Rabi Yacoub, Rudiyanto Gunawan, John E. Tomaszewski, Pinaki Sarder:
  Integrating image analysis with single cell RNA-seq data to study podocyte-specific changes in diabetic kidney disease. Digital and Computational Pathology 2022
• 
  Ahmet Gokberk Gul, Özdemir Çetin, Christoph Reich, Nadine Flinner, Tim Prangemeier, Heinz Koeppl:
  Histopathological image classification based on self-supervised vision transformer and weak labels. Digital and Computational Pathology 2022
• 
  Mauro Gwerder, Amjad Khan, Christina Neppl, Inti Zlobec:
  Detection of lung cancer metastases in lymph nodes using a multiple instance learning approach. Digital and Computational Pathology 2022
• 
  Wenchao Han, Alison M. Cheung, Dan Wang, Kela Liu, Martin J. Yaffe, Anne L. Martel:
  Cell phenotyping using unsupervised clustering on multiplexed fluorescence images of breast cancer tissue specimens. Digital and Computational Pathology 2022
• 
  Chang Hee Han, Jin Tae Kwak:
  A hybrid computational pathology method for the detection of perineural invasion junctions. Digital and Computational Pathology 2022
• 
  Laurin Herbsthofer, Barbara Ehall, Martina Tomberger, Barbara Prietl, Thomas R. Pieber, Pablo López-García:
  Procedural generation of synthetic multiplex immunohistochemistry images using cell-based image compression and conditional generative adversarial networks. Digital and Computational Pathology 2022
• 
  Saarthak Kapse, Rajarsi Gupta, Prateek Prasanna:
  Shape-based tumor microenvironment analysis to differentiate non-small cell lung cancer subtypes: a radio-pathomic study. Digital and Computational Pathology 2022
• 
  Neil Kavthekar, Brandon Ginley, Samuel P. Border, Nicholas J. Lucarelli, Kuang-Yu Jen, Pinaki Sarder:
  Automated tubular morphometric visualization for whole kidney biopsy. Digital and Computational Pathology 2022
• 
  Jun Li, Zhiguo Jiang, Yushan Zheng, Haopeng Zhang, Jun Shi, Dingyi Hu, Wei Luo, Zhongmin Jiang, Chenghai Xue:
  Weakly supervised histopathological image representation learning based on contrastive dynamic clustering. Digital and Computational Pathology 2022

skipping 31 more matches