Shahira Abousamra
Shahira Abousamra
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Localization in the crowd with topological constraints
S Abousamra, M Hoai, D Samaras, C Chen
Proceedings of the AAAI Conference on Artificial Intelligence 35 (2), 872-881, 2021
Utilizing automated breast cancer detection to identify spatial distributions of tumor-infiltrating lymphocytes in invasive breast cancer
H Le, R Gupta, L Hou, S Abousamra, D Fassler, L Torre-Healy, RA Moffitt, ...
The American journal of pathology 190 (7), 1491-1504, 2020
Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images
DJ Fassler, S Abousamra, R Gupta, C Chen, M Zhao, D Paredes, ...
Diagnostic pathology 15 (1), 1-11, 2020
Deep learning-based mapping of tumor infiltrating lymphocytes in whole slide images of 23 types of cancer
S Abousamra, R Gupta, L Hou, R Batiste, T Zhao, A Shankar, A Rao, ...
Frontiers in oncology 11, 806603, 2022
Weakly-supervised deep stain decomposition for multiplex IHC images
S Abousamra, D Fassler, L Hou, Y Zhang, R Gupta, T Kurc, ...
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 481-485, 2020
Multi-class cell detection using spatial context representation
S Abousamra, D Belinsky, J Van Arnam, F Allard, E Yee, R Gupta, T Kurc, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
Exascale deep learning to accelerate cancer research
RM Patton, JT Johnston, SR Young, CD Schuman, TE Potok, DC Rose, ...
2019 IEEE International Conference on Big Data (Big Data), 1488-1496, 2019
Federated learning for the classification of tumor infiltrating lymphocytes
U Baid, S Pati, TM Kurc, R Gupta, E Bremer, S Abousamra, SP Thakur, ...
arXiv preprint arXiv:2203.16622, 2022
Learning from thresholds: fully automated classification of tumor infiltrating lymphocytes for multiple cancer types
S Abousamra, L Hou, R Gupta, C Chen, D Samaras, T Kurc, R Batiste, ...
arXiv preprint arXiv:1907.03960, 2019
GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows
S Pati, SP Thakur, İE Hamamcı, U Baid, B Baheti, M Bhalerao, O Güley, ...
Communications Engineering 2 (1), 23, 2023
Localization and tracking in 4D fluorescence microscopy imagery
S Abousamra, S Adar, N Elia, R Shilkrot
Proceedings of the IEEE conference on computer vision and pattern …, 2018
Fair and adaptive online set-based cache partitioning
S Abousamra, A El-Mahdy, S Selim
The 2011 International Conference on Computer Engineering & Systems, 9-16, 2011
ChampKit: a framework for rapid evaluation of deep neural networks for patch-based histopathology classification
JR Kaczmarzyk, R Gupta, TM Kurc, S Abousamra, JH Saltz, PK Koo
Computer Methods and Programs in Biomedicine, 107631, 2023
Xiao Ling Hu
C Li, X Hu, S Abousamra, C Chen, A Konwer, X Xu, J Bae, P Prasanna, ...
Calibrating uncertainty for semi-supervised crowd counting
C Li, X Hu, S Abousamra, C Chen
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 16685-16695, 2023
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
S Abousamra, R Gupta, T Kurc, D Samaras, J Saltz, C Chen
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Evaluating histopathology transfer learning with ChampKit
JR Kaczmarzyk, TM Kurc, S Abousamra, R Gupta, JH Saltz, PK Koo
arXiv preprint arXiv:2206.06862, 2022
An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities
DJ Foran, EB Durbin, W Chen, E Sadimin, A Sharma, I Banerjee, T Kurc, ...
Journal of Pathology Informatics 13, 100167, 2022
From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer
T Kurc, A Sharma, R Gupta, L Hou, H Le, S Abousamra, E Bremer, ...
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2020
Unsupervised Stain Decomposition via Inversion Regulation for Multiplex Immunohistochemistry Images
S Abousamra, D Fassler, J Yao, RR Gupta, T Kurc, L Escobar-Hoyos, ...
Medical Imaging with Deep Learning, 2023
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