Search Results - "Bao, Rina"

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning by Hajilounezhad, Taher, Bao, Rina, Palaniappan, Kannappan, Bunyak, Filiz, Calyam, Prasad, Maschmann, Matthew R.

“…Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad…”
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GLSNet++: Global and Local-Stream Feature Fusion for LiDAR Point Cloud Semantic Segmentation Using GNN Demixing Block by Bao, Rina, Palaniappan, Kannappan, Zhao, Yunxin, Seetharaman, Guna

“…Semantic point cloud segmentation is a critical task in 3-D computer vision, offering valuable contextual information for navigation, cartography, landmarks,…”
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Lightweight SDE-Net Fusing Model-Based and Learned Features for Computational Histopathology by Bao, Rina, Zhao, Yunxin, Srivastava, Akhil, Frazier, Shellaine, Palaniappan, Kannappan

“…Model-based deep learning has the potential to significantly reduce the size of deep architectures while matching the competitive performance of much deeper…”
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The Cell Tracking Challenge: 10 years of objective benchmarking by Maška, Martin, Ulman, Vladimír, Delgado-Rodriguez, Pablo, Gómez-de-Mariscal, Estibaliz, Nečasová, Tereza, Guerrero Peña, Fidel A., Ren, Tsang Ing, Meyerowitz, Elliot M., Scherr, Tim, Löffler, Katharina, Mikut, Ralf, Guo, Tianqi, Wang, Yin, Allebach, Jan P., Bao, Rina, Al-Shakarji, Noor M., Rahmon, Gani, Toubal, Imad Eddine, Palaniappan, Kannappan, Lux, Filip, Matula, Petr, Sugawara, Ko, Magnusson, Klas E. G., Aho, Layton, Cohen, Andrew R., Arbelle, Assaf, Ben-Haim, Tal, Raviv, Tammy Riklin, Isensee, Fabian, Jäger, Paul F., Maier-Hein, Klaus H., Zhu, Yanming, Ederra, Cristina, Urbiola, Ainhoa, Meijering, Erik, Cunha, Alexandre, Muñoz-Barrutia, Arrate, Kozubek, Michal, Ortiz-de-Solórzano, Carlos

“…The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we…”
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Deep Learning Architectures for 2D and 3D Scene Perception by Bao, Rina

“…Scene understanding is a fundamental problem in computer vision tasks, that is being more intensively explored in recent years with the development of deep…”
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DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking by Bao, Rina, Al-Shakarji, Noor M., Bunyak, Filiz, Palaniappan, Kannappan

“…Accurate segmentation and tracking of cells in microscopy image sequences is extremely beneficial in clinical diagnostic applications and biomedical research…”
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GLSNet: Global and Local Streams Network for 3D Point Cloud Classification by Bao, Rina, Palaniappan, Kannappan, Zhao, Yunxin, Seetharaman, Guna, Zeng, Wenjun

“…We propose a novel deep architecture for semantic labeling of 3D point clouds referred to as Global and Local Streams Network (GLSNet) which is designed to…”
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P18-006-23 Maternal Dietary Nutrition Interact With Demographic and Socioeconomic Information To Impact Offspring Neurocognition by Early School Age by Bao, Rina, Wu, Di, Vyas, Rutvi, Kuchan, Matthew, Lasekan, John, Sutton, Brad, Grant, Ellen, Morton, Sarah, Ou, Yangming

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BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): II. 2-year Neurocognitive Outcome and NICU Outcome by Bao, Rina, Ou, Yangming

“…Hypoxic Ischemic Encephalopathy (HIE) affects approximately 1-5/1000 newborns globally and leads to adverse neurocognitive outcomes in 30% to 50% of cases by…”
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AGE2HIE: Transfer Learning from Brain Age to Predicting Neurocognitive Outcome for Infant Brain Injury by Bao, Rina, He, Sheng, Grant, Ellen, Ou, Yangming

“…Hypoxic-Ischemic Encephalopathy (HIE) affects 1 to 5 out of every 1,000 newborns, with 30% to 50% of cases resulting in adverse neurocognitive outcomes…”
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U-Netmer: U-Net meets Transformer for medical image segmentation by He, Sheng, Bao, Rina, Grant, P. Ellen, Ou, Yangming

“…The combination of the U-Net based deep learning models and Transformer is a new trend for medical image segmentation. U-Net can extract the detailed local…”
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Foundation AI Model for Medical Image Segmentation by Bao, Rina, Darzi, Erfan, He, Sheng, Hsiao, Chuan-Heng, Hussain, Mohammad Arafat, Li, Jingpeng, Bjornerud, Atle, Grant, Ellen, Ou, Yangming

“…Foundation models refer to artificial intelligence (AI) models that are trained on massive amounts of data and demonstrate broad generalizability across…”
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Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets by He, Sheng, Bao, Rina, Li, Jingpeng, Stout, Jeffrey, Bjornerud, Atle, Grant, P. Ellen, Ou, Yangming

“…Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural…”
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