Výsledky vyhledávání - "Nabavi, Sheida"

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Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images

Autor: Li, Bingjun, Karami, Mostafa, Junayed, Masum Shah, Nabavi, Sheida

Understanding the intricate cellular environment within biological tissues is crucial for uncovering insights into complex biological functions. While single-cell RNA sequencing has significantly enhanced our understanding of cellular states, it lack

Externí odkaz: http://arxiv.org/abs/2411.02534

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Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammograms

Autor: Bai, Jun, Jin, Annie, Adams, Madison, Yang, Clifford, Nabavi, Sheida

Breast cancer continues to be a significant cause of mortality among women globally. Timely identification and precise diagnosis of breast abnormalities are critical for enhancing patient prognosis. In this study, we focus on improving the early dete

Externí odkaz: http://arxiv.org/abs/2312.16772

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A Multimodal Graph Neural Network Framework of Cancer Molecular Subtype Classification

Autor: Li, Bingjun, Nabavi, Sheida

The recent development of high-throughput sequencing creates a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics da

Externí odkaz: http://arxiv.org/abs/2302.12838

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Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images

Autor: Behzadi, Mohammad Mahdi, Madani, Mohammad, Wang, Hanzhang, Bai, Jun, Bhardwaj, Ankit, Tarakanova, Anna, Yamase, Harold, Nam, Ga Hie, Nabavi, Sheida

Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assi

Externí odkaz: http://arxiv.org/abs/2212.12844

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