Ovidiu Daescu
University of Texas at Dallas
161 Papers
520 Citations
Ovidiu Daescu is an academic researcher from University of Texas at Dallas. The author has contributed to research in topics: Approximation algorithm & Shortest path problem. The author has an hindex of 18, co-authored 146 publications. Previous affiliations of Ovidiu Daescu include University of Notre Dame.
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Papers
The human red blood cell proteome and interactome.
TL;DR: This minireview discusses alterations in this partial proteome in varied human disease states, and demonstrates how in silico studies of the RBC interactome can lead to considerable insight into disease diagnosis, severity, and drug or gene therapy response.
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Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models
Harish Babu Arunachalam,Rashika Mishra,Ovidiu Daescu,Kevin B Cederberg,Kevin B Cederberg,Dinesh Rakheja,Dinesh Rakheja,Anita L Sengupta,Anita L Sengupta,David Leonard,Rami R. Hallac,Patrick J. Leavey,Patrick J. Leavey +12 more
TL;DR: This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digitization and automated learning to lay the foundation for a complete tumor assessment pipeline from original histology images to tumor-prediction map generation.
Convolutional Neural Network for Histopathological Analysis of Osteosarcoma
TL;DR: This article proposes convolutional neural network as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor and compares the proposed architecture with three existing and proven CNN architectures for image classification.
84
Efficiently approximating polygonal paths in three and higher dimensions
Gill Barequet,Michael T. Goodrich,Danny Z. Chen,Ovidiu Daescu,Jack Snoeyink +4 more
- 07 Jun 1998
TL;DR: This work presents efficient algorithms for solving polygonal-path approximation problems in three and higher dimensions and develops efficient near-quadratic-time and subcubic-time algorithms in four dimensions for solving the min-# and min-" problems.
Deep learning for skin lesion segmentation
Rashika Mishra,Ovidiu Daescu +1 more
- 01 Nov 2017
TL;DR: A deep learning method for automatic skin lesion segmentation that can outperform the submissions in terms of segmentation accuracy and compare against the benchmark results submitted by other participants.
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