Výsledky vyhledávání - "Guo, Bangwei"

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DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture

Autor: Cen, Min, Li, Xingyu, Guo, Bangwei, Jonnagaddala, Jitendra, Zhang, Hong, Xu, Xu Steven

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. In this study, we developed a novel and efficient digi

Externí odkaz: http://arxiv.org/abs/2305.01968

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Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines

Autor: Cen, Min, Li, Xingyu, Guo, Bangwei, Jonnagaddala, Jitendra, Zhang, Hong, Xu, Xu Steven

NLP-based computer vision models, particularly vision transformers, have been shown to outperform CNN models in many imaging tasks. However, most digital pathology artificial-intelligence models are based on CNN architectures, probably owing to a lac

Externí odkaz: http://arxiv.org/abs/2302.10406

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Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers

Autor: Liu, Anran, Li, Xingyu, Wu, Hongyi, Guo, Bangwei, Jonnagaddala, Jitendra, Zhang, Hong, Xu, Xu Steven

Purpose Tumor-infiltrating lymphocytes (TILs) have significant prognostic values in cancers. However, very few automated, deep-learning-based TIL scoring algorithms have been developed for colorectal cancers (CRC). Methods We developed an automated,

Externí odkaz: http://arxiv.org/abs/2208.11518

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Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer

Autor: Guo, Bangwei, Li, Xingyu, Jonnagaddala, Jitendra, Zhang, Hong, Xu, Xu Steven

Artificial intelligence (AI) models have been developed for predicting clinically relevant biomarkers, including microsatellite instability (MSI), for colorectal cancers (CRC). However, the current deep-learning networks are data-hungry and require l

Externí odkaz: http://arxiv.org/abs/2208.10495

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A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations

Autor: Guo, Bangwei, Li, Xingyu, Yang, Miaomiao, Zhang, Hong, Xu, Xu Steven

Deep-learning models based on whole-slide digital pathology images (WSIs) become increasingly popular for predicting molecular biomarkers. Instance-based models has been the mainstream strategy for predicting genetic alterations using WSIs although b

Externí odkaz: http://arxiv.org/abs/2206.00455

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