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Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI

IEEE transactions on biomedical engineering, 2021-02, Vol.68 (2), p.374-383

2021

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Digital Pathology: 15th European Congress, ECDP 2019, Warwick, UK, April 10-13, 2019, Proceedings

1, 2019

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Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

IEEE transactions on medical imaging, 2021-12, Vol.40 (12), p.3543-3554

2021

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Roto-Translation Covariant Convolutional Networks for Medical Image Analysis

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, p.440-448

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Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, p.83-91

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Breast Cancer Histopathology Image Analysis: A Review

IEEE transactions on biomedical engineering, 2014-05, Vol.61 (5), p.1400-1411

2014

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Progressively Trained Convolutional Neural Networks for Deformable Image Registration

IEEE transactions on medical imaging, 2020-05, Vol.39 (5), p.1594-1604

2020

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Automatic nuclei segmentation in H&E stained breast cancer histopathology images

PloS one, 2013-07, Vol.8 (7), p.e70221-e70221

2013

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Intensity Augmentation to Improve Generalizability of Breast Segmentation Across Different MRI Scan Protocols

IEEE transactions on biomedical engineering, 2021-03, Vol.68 (3), p.759-770

2021

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Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method

PloS one, 2016-08, Vol.11 (8), p.e0161286-e0161286

2016

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Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI

Journal of magnetic resonance imaging, 2020-06, Vol.51 (6), p.1689-1696

2020

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Automated clear cell renal carcinoma grade classification with prognostic significance

PloS one, 2019-10, Vol.14 (10), p.e0222641-e0222641

2019

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Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning

Scientific reports, 2022-07, Vol.12 (1), p.11334-12, Article 11334

2022

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Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images

Scientific reports, 2022-09, Vol.12 (1), p.15102-12, Article 15102

2022

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Corneal pachymetry by AS-OCT after Descemet’s membrane endothelial keratoplasty

Scientific reports, 2021-07, Vol.11 (1), p.13976-13976, Article 13976

2021

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Pros and cons of artificial intelligence implementation in diagnostic pathology

Histopathology, 2024-05, Vol.84 (6), p.924-934

2024

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Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

Laboratory investigation, 2021-04, Vol.101 (4), p.525-533

2021

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A review of machine learning applications for the proton MR spectroscopy workflow

Magnetic resonance in medicine, 2023-10, Vol.90 (4), p.1253-1270

2023

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Whole‐slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma

Experimental dermatology, 2021-05, Vol.30 (5), p.733-738

2021

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Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning

PloS one, 2023-06, Vol.18 (6), p.e0279525-e0279525

2023

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Learning Domain-Invariant Representations of Histological Images

Frontiers in medicine, 2019-07, Vol.6, p.162

2019

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Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk

PloS one, 2020-04, Vol.15 (4), p.e0231653-e0231653

2020

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A comprehensive multi-domain dataset for mitotic figure detection

Scientific data, 2023-07, Vol.10 (1), p.484-12, Article 484

2023

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Corrigendum to “Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge” [Medical Image Analysis, 54 (2019) 111--121]

Medical image analysis, 2019-08, Vol.56, p.43-43

Veta, Mitko

2019

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Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI

Digital Pathology: 15th European Congress, ECDP 2019, Warwick, UK, April 10-13, 2019, Proceedings

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

Roto-Translation Covariant Convolutional Networks for Medical Image Analysis

Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images

Breast Cancer Histopathology Image Analysis: A Review

Progressively Trained Convolutional Neural Networks for Deformable Image Registration

Automatic nuclei segmentation in H&E stained breast cancer histopathology images

Intensity Augmentation to Improve Generalizability of Breast Segmentation Across Different MRI Scan Protocols

Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method

Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI

Assessment of algorithms for mitosis detection in breast cancer histopathology images

Automated clear cell renal carcinoma grade classification with prognostic significance

Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning

Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images

Corneal pachymetry by AS-OCT after Descemet’s membrane endothelial keratoplasty

Pros and cons of artificial intelligence implementation in diagnostic pathology

Deep learning-based grading of ductal carcinoma in situ in breast histopathology images

A review of machine learning applications for the proton MR spectroscopy workflow

Whole‐slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning

Learning Domain-Invariant Representations of Histological Images

Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk

A comprehensive multi-domain dataset for mitotic figure detection

Corrigendum to “Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge” [Medical Image Analysis, 54 (2019) 111--121]

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