D
Dimitris Samaras
Researcher at Stony Brook University
Publications - 268
Citations - 11406
Dimitris Samaras is an academic researcher from Stony Brook University. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 51, co-authored 234 publications receiving 8825 citations. Previous affiliations of Dimitris Samaras include École Centrale Paris & Chinese Academy of Sciences.
Papers
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Journal ArticleDOI
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.
Joel H. Saltz,Rajarsi Gupta,Le Hou,Tahsin Kurc,Pankaj Singh,Vu Nguyen,Dimitris Samaras,Kenneth R. Shroyer,Tianhao Zhao,Rebecca Batiste,John Van Van Arnam,Ilya Shmulevich,Arvind Rao,Alexander J. Lazar,Ashish Sharma,Vesteinn Thorsson +15 more
TL;DR: Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.
Proceedings ArticleDOI
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
TL;DR: A novel Expectation-Maximization (EM) based method is formulated that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches and applies it to the classification of glioma and non-small-cell lung carcinoma cases into subtypes.
Journal ArticleDOI
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham,Michael P. Milham,Adriana Di Martino,R. Cameron Craddock,Dimitris Samaras,Bertrand Thirion,Gaël Varoquaux +6 more
TL;DR: The feasibility of inter‐site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi‐site autism dataset is demonstrated.
Proceedings ArticleDOI
Two-person interaction detection using body-pose features and multiple instance learning
TL;DR: A complex human activity dataset depicting two person interactions, including synchronized video, depth and motion capture data is created, and techniques related to Multiple Instance Learning (MIL) are explored, finding that the MIL based classifier outperforms SVMs when the sequences extend temporally around the interaction of interest.
Journal ArticleDOI
Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics
Lei Zhang,Dimitris Samaras +1 more
TL;DR: Two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information are proposed.