• 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 492 matches

• 2023
• 
  Areej Alsaafin, Morteza Babaie, Hamid R. Tizhoosh:
  Deep modality association learning using histopathology images and immune cell sequencing data. Digital and Computational Pathology 2023
• 
  Arian Arab, Victor Garcia, Shuyue Guan, Brandon D. Gallas, Berkman Sahiner, Nicholas Petrick, Weijie Chen:
  Effect of color-normalization on deep learning segmentation models for tumor-infiltrating lymphocytes scoring using breast cancer histopathology images. Digital and Computational Pathology 2023
• 
  J. Arslan, Mehdi Ounissi, H. Luo, M. Lacroix, P. Dupré, P. Kumar, Arran Hodgkinson, S. Dandou, Romain M. Larive, C. Pignodel, L. Le Cam, Ovidiu Radulescu, Daniel Racoceanu:
  Efficient 3D reconstruction of whole slide images in melanoma. Digital and Computational Pathology 2023
• 
  Shunxing Bao, Can Cui, Jia Li, Yucheng Tang, Ho Hin Lee, Ruining Deng, Lucas W. Remedios, Xin Yu, Qi Yang, Sophie Chiron, Nathan Heath Patterson, Ken S. Lau, Qi Liu, Joseph T. Roland, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo:
  Topological-preserving membrane skeleton segmentation in multiplex immunofluorescence imaging. Digital and Computational Pathology 2023
• 
  Roozbeh Bazargani, Wanwen Chen, Sadaf Sadeghian, Maryam Asadi, Jeffrey Boschman, Amirali Darbandsari, Ali Bashashati, Septimiu E. Salcudean:
  A novel H&E color augmentation for domain invariance classification of unannotated histopathology prostate cancer images. Digital and Computational Pathology 2023
• 
  Chang Bian, Tim Cootes, Martin Fergie:
  A transformer-based computational approach for H&E to multiplexed immunohistochemistry stain translation. Digital and Computational Pathology 2023
• 
  Tanwi Biswas, Hiroyuki Suzuki, Masahiro Ishikawa, Naoki Kobayashi, Takashi Obi:
  Generation of RGB EVG stained image from hyperspectral H&E stained image using generative adversarial network (GAN). Digital and Computational Pathology 2023
• 
  Samuel P. Border, Avi Rosenberg, Jarcy Zee, Richard M. Levenson, Kuang-Yu Jen, Pinaki Sarder, Farzad Fereidouni:
  Improving quantification of renal fibrosis using deep-DUET. Digital and Computational Pathology 2023
• 
  Jack Breen, Katie Allen, Kieran Zucker, Geoff Hall, Nicolas M. Orsi, Nishant Ravikumar:
  Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning. Digital and Computational Pathology 2023
• 
  Alexander F. B. Carmichael, Johanna L. Baily, Aaron Reeves, Gabriela Ochoa, Annette S. Boerlage, George J. Gunn, Rosa Allshire, Deepayan Bhowmik:
  Analysing hyperplasia in Atlantic salmon gills using empirical wavelets. Digital and Computational Pathology 2023
• 
  Santiago Castro, David Romo-Bucheli, Luis Guayacán, Fabio Martínez:
  Fast detection and localization of mitosis using a semi-supervised deep representation. Digital and Computational Pathology 2023
• 
  Alison M. Cheung, Hassan Besher, Dan Wang, Kela Liu, Yutaka Amemiya, Jianan Chen, Anne L. Martel, Arun Seth, Martin J. Yaffe:
  Integrated image-processing and transcriptomic analysis of cancer-associated fibroblasts (CAFs) in breast cancer subtypes. Digital and Computational Pathology 2023
• 
  Germán Corredor, Can Koyuncu, Andrew Janowczyk, Paula Toro, Sepideh Azarianpour, James S. Lewis Jr., Anant Madabhushi:
  Spatial connectivity of tumor and associated cells (SpaCell): a novel computational pathology biomarker. Digital and Computational Pathology 2023
• 
  Can Cui, Shunxing Bao, Jia Li, Ruining Deng, Lucas W. Remedios, Zuhayr Asad, Sophie Chiron, Ken S. Lau, Yaohong Wang, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Qi Liu, Yuankai Huo:
  Influence of cell-type ratio on spatially resolved single-cell transcriptomes using the Tangram algorithm: based on implementation on single-cell and MxIF data. Digital and Computational Pathology 2023
• 
  Palak Dave, Yaroslav Kolinko, Hunter Morera, Kurtis Allen, Saeed S. Alahmari, Dmitry B. Goldgof, Lawrence O. Hall, Peter R. Mouton:
  MIMO U-Net: efficient cell segmentation and counting in microscopy image sequences. Digital and Computational Pathology 2023
• 
  Cihan Erkan, Selim Aksoy:
  Space-filling curves for modeling spatial context in transformer-based whole slide image classification. Digital and Computational Pathology 2023
• 
  Brennan T. Flannery, Priti Lal, Michael D. Feldman, María Natalizio, Juan C. Santa-Rosario, Anant Madabhushi:
  Biopsy and surgical specimen specific deep learning models for prostate cancer detection on digitized pathology images. Digital and Computational Pathology 2023
• 
  Jonathan Folmsbee, Margaret Brandwein-Weber, Scott Doyle:
  Combining multiple ground truth annotations for segmentation training for oral cavity cancer. Digital and Computational Pathology 2023
• 
  Brandon Ginley, Nicholas J. Lucarelli, Jarcy Zee, Sanjay Jain, Seung Seok Han, Luis Rodrigues, Michelle L. Wong, Kuang-Yu Jen, Pinaki Sarder:
  Automated reference kidney histomorphometry using a panoptic segmentation neural network correlates to patient demographics and creatinine. Digital and Computational Pathology 2023
• 
  Paras Goel, Saarthak Kapse, Prateek Prasanna:
  Role of stain normalization in computational pathology: use case in metastatic tissue classification. Digital and Computational Pathology 2023
• 
  Naga Raju Gudhe, Mazen Sudah, Arto Mannermaa, Veli-Matti Kosma, Hamid Behravan:
  Predicting cell type counts in whole slide histology images using evidential multi-task learning. Digital and Computational Pathology 2023
• 
  Hooria Hajiyan, Mehran Ebrahimi:
  Multi-scale local explanation approach for image analysis using model-agnostic Explainable Artificial Intelligence (XAI). Digital and Computational Pathology 2023
• 
  Wenchao Han, Alison M. Cheung, Vishwesh Ramanathan, Dan Wang, Kela Liu, Martin J. Yaffe, Anne L. Martel:
  Identification of molecular cell type of breast cancer on digital histopathology images using deep learning and multiplexed fluorescence imaging. Digital and Computational Pathology 2023
• 
  Laurin Herbsthofer, Barbara Ehall, Martina Tomberger, Barbara Prietl, Thomas R. Pieber, Pablo López-García:
  Cell2Voxel: a novel, cell-based 3D tissue model from 2D multiplex tissue scans. Digital and Computational Pathology 2023
• 
  Chao-Hui Huang, Yoson Park, Jincheng Pang, Jadwiga R. Bienkowska:
  Single-cell gene expression prediction using H&E images based on spatial transcriptomics. Digital and Computational Pathology 2023
• 
  Gabriel Jimenez, Pablo Mas, Anuradha Kar, Julien Peyrache, Léa Ingrassia, Susana Boluda, Benoît Delatour, Lev Stimmer, Daniel Racoceanu:
  A meta-graph approach for analyzing whole slide histopathological images of human brain tissue with Alzheimer's disease biomarkers. Digital and Computational Pathology 2023
• 
  Seyed M. M. Kahaki, Ian S. Hagemann, Kenny H. Cha, Christopher J. Trindade, Nicholas Petrick, Nicolas Kostelecky, Weijie Chen:
  Weakly supervised deep learning for predicting the response to hormonal treatment of women with atypical endometrial hyperplasia: a feasibility study. Digital and Computational Pathology 2023
• 
  Jason Keighley, Marc de Kamps, Alexander I. Wright, Darren Treanor:
  Digital pathology whole slide image compression with vector quantized variational autoencoders. Digital and Computational Pathology 2023
• 
  Sena Korkut, Cihan Erkan, Selim Aksoy:
  On the benefits of region of interest detection for whole slide image classification. Digital and Computational Pathology 2023
• 
  Olivia Krebs, Shobhit Agarwal, Pallavi Tiwari:
  Self-supervised deep learning to predict molecular markers from routine histopathology slides for high-grade glioma tumors. Digital and Computational Pathology 2023

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