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A survey on deep learning in medical image analysis

Medical image analysis, 2017-12, Vol.42, p.60-88

2017

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Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies

Modern pathology, 2018-10, Vol.31 (10), p.1502-1512

2018

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Deep learning assisted mitotic counting for breast cancer

Laboratory investigation, 2019-11, Vol.99 (11), p.1596-1606

2019

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Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning

Laboratory investigation, 2021-08, Vol.101 (8), p.970-982

2021

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Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning

NPJ breast cancer, 2022-01, Vol.8 (1), p.13-13, Article 13

2022

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Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm

Diagnostics (Basel), 2022-02, Vol.12 (2), p.436

2022

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A Decade of GigaScience: The Challenges of Gigapixel Pathology Images

Gigascience, 2022-06, Vol.11

2022

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Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer

Cellular oncology (Dordrecht), 2019-08, Vol.42 (4), p.555-569

2019

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Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

IEEE transactions on medical imaging, 2018-09, Vol.37 (9), p.2126-2136

2018

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IEEE_Xplore

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Scientific reports, 2016-05, Vol.6 (1), p.26286-26286, Article 26286

2016

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1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset

Gigascience, 2018-06, Vol.7 (6)

2018

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Artificial intelligence applied to breast pathology

Virchows Archiv : an international journal of pathology, 2022, Vol.480 (1), p.191-209

2022

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Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

PeerJ (San Francisco, CA), 2019-12, Vol.7, p.e8242-e8242, Article e8242

2019

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PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning

Breast cancer research : BCR, 2023-11, Vol.25 (1), p.1-142, Article 142

2023

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Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks

PeerJ (San Francisco, CA), 2019-04, Vol.7, p.e6335-e6335, Article e6335

2019

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Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

NPJ digital medicine, 2022-07, Vol.5 (1), p.102-102, Article 102

2022

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Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer

Cancers, 2023-03, Vol.15 (7), p.2079

2023

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Neural Image Compression for Gigapixel Histopathology Image Analysis

IEEE transactions on pattern analysis and machine intelligence, 2021-02, Vol.43 (2), p.567-578

2021

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IEEE_Xplore

Automated Deep Learning-Based Classification of Wilms Tumor Histopathology

Cancers, 2023-05, Vol.15 (9), p.2656

2023

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Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer

NPJ breast cancer, 2022-11, Vol.8 (1), p.120-120, Article 120

2022

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HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

Medical image analysis, 2021-02, Vol.68, p.101890-101890, Article 101890

2021

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Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Medical image analysis, 2019-12, Vol.58, p.101544-101544, Article 101544

2019

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Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density

NPJ breast cancer, 2019-11, Vol.5 (1), p.43-11, Article 43

2019

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Comparison of different methods for tissue segmentation in histopathological whole-slide images

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, p.591-595

2017

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IEEE_Xplore

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A survey on deep learning in medical image analysis

Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies

Deep learning assisted mitotic counting for breast cancer

Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning

Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm

A Decade of GigaScience: The Challenges of Gigapixel Pathology Images

Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset

Artificial intelligence applied to breast pathology

Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning

Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer

Neural Image Compression for Gigapixel Histopathology Image Analysis

Automated Deep Learning-Based Classification of Wilms Tumor Histopathology

Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer

HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density

Comparison of different methods for tissue segmentation in histopathological whole-slide images

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