BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images-Reference-Cited by-同舟云学术

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BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Published:2021-07 Issue: Volume: Page:
ISSN:1672-0229
Container-title:Genomics, Proteomics & Bioinformatics
language:en
Short-container-title:Genomics, Proteomics & Bioinformatics

Author:

Lu Zixiao,Zhan Xiaohui,Wu Yi,Cheng Jun,Shao Wei,Ni Dong,Han Zhi,Zhang Jie,Feng Qianjin,Huang Kun

Publisher

Elsevier BV

Subject

Computational Mathematics,Genetics,Molecular Biology,Biochemistry

Reference36 articles.

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2. Tumor–stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients;de Kruijf;Breast Cancer Res Treat,2011

3. Prognostic significance of tumor-infiltrating lymphocytes in ductal carcinoma in situ of the breast;Toss;Mod Pathol,2018

4. Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixel-based segmentation;Fouad;J Imaging,2017

5. Interactive segmentation relabeling for classification of whole-slide histopathology imagery;Haridas,2015

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2. Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm;Medical Molecular Morphology;2024-08-01

3. Computer-aided diagnosis using white shark optimizer with attention-based deep learning for breast cancer classification;Journal of Intelligent & Fuzzy Systems;2023-08-01

4. hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images;Cancers;2023-04-30

5. Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology;npj Breast Cancer;2023-04-06

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