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Mohammed Alawad
Researcher at Oak Ridge National Laboratory
Publications - 43
Citations - 397
Mohammed Alawad is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 8, co-authored 35 publications receiving 185 citations. Previous affiliations of Mohammed Alawad include Wayne State University & University of Central Florida.
Papers
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Journal ArticleDOI
Limitations of Transformers on Clinical Text Classification
Shang Gao,Mohammed Alawad,M. Todd Young,John Gounley,Noah Schaefferkoetter,Hong-Jun Yoon,Xiao-Cheng Wu,Eric B. Durbin,Jennifer A. Doherty,Antoinette M. Stroup,Linda Coyle,Georgia D. Tourassi +11 more
TL;DR: In this article, the authors introduce four methods to scale BERT, which by default can only handle input sequences up to approximately 400 words long, to perform document classification on clinical texts several thousand words long.
Journal ArticleDOI
Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks.
Mohammed Alawad,Shang Gao,John X. Qiu,Hong-Jun Yoon,J. Blair Christian,Lynne Penberthy,Brent J. Mumphrey,Xiao-Cheng Wu,Linda Coyle,Georgia D. Tourassi +9 more
TL;DR: The hard parameter sharing MTCNN offers superior classification accuracy for automated coding support of pathology documents across a wide range of cancers and multiple information extraction tasks while maintaining similar training and inference time as those of a single task–specific model.
Journal ArticleDOI
Classifying cancer pathology reports with hierarchical self-attention networks.
Shang Gao,John X. Qiu,Mohammed Alawad,Jacob Hinkle,Noah Schaefferkoetter,Hong-Jun Yoon,Blair Christian,Paul A. Fearn,Lynne Penberthy,Xiao-Cheng Wu,Linda Coyle,Georgia D. Tourassi,Arvind Ramanathan +12 more
TL;DR: This work introduces a deep learning architecture, hierarchical self-attention networks (HiSANs), designed for classifying pathology reports and shows how its unique architecture leads to a new state-of-the-art in accuracy, faster training, and clear interpretability.
Journal ArticleDOI
Stochastic-Based Deep Convolutional Networks with Reconfigurable Logic Fabric
Mohammed Alawad,Mingjie Lin +1 more
TL;DR: This work presents a novel stochastic-based and scalable hardware architecture and circuit design that computes a large-scale CNN with FPGA that is well-suited for a modular vision engine with the goal of performing real-time detection, recognition, and segmentation of mega-pixel images.
Proceedings ArticleDOI
Coarse-to-fine multi-task training of convolutional neural networks for automated information extraction from cancer pathology reports
TL;DR: An automated approach using a coarse-to-fine training of convolutional neural networks for extracting the primary site, histological grade and laterality from unstructured cancer pathology text reports consistently outperformed the base line models, especially for the less prevalent classes.