| [1] |
Metter DM, Colgan TJ, Leung ST, et al. Trends in the US and Canadian Pathologist Workforces From 2007 to 2017 [J]. JAMA Netw Open, 2019, 2(5): e194337.
|
| [2] |
Williams B J, Lee J, Oien K A, et al. Digital pathology access and usage in the UK: results from a national survey on behalf of the National Cancer Research Institute's CM-Path initiative [J]. Journal of Clinical Pathology, 2018, 71(5): 463-466.
|
| [3] |
LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436-444.
|
| [4] |
Kamilaris A, Prenafeta-Boldú FX. Deep learning in agriculture: A survey [J]. Computers and Electronics in Agriculture, 2018, 147: 70-90.
|
| [5] |
Helber P, Bischke B, Dengel A, et al. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(7): 2217-2226.
|
| [6] |
Shen D, Wu G, Suk HI. Deep Learning in Medical Image Analysis [J]. Annu Rev Biomed Eng, 2017, 19: 221-248.
|
| [7] |
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis [J]. Med Image Anal, 2017, 42: 60-88.
|
| [8] |
Ortiz A, Munilla J, Górriz JM, et al. Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease [J]. Int J Neural Syst, 2016, 26(7): 1650025.
|
| [9] |
Milletari F, Ahmadi S A, Kroll C, et al. Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound [J]. Computer Vision and Image Understanding, 2017, 164: 92-102.
|
| [10] |
Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods [J]. Procedia Computer Science, 2016, 102: 317-324.
|
| [11] |
Cha KH, Hadjiiski L, Samala RK, et al. Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets [J]. Med Phys, 2016, 43(4): 1882.
|
| [12] |
Roth H R, Farag A, Lu L, et al. Deep convolutional networks for pancreas segmentation in CT imaging. Medical Imaging 2015: Image Processing [J]. International Society for Optics and Photonics, 2015, 9413: 94131G.
|
| [13] |
Li X, Chen H, Qi X, et al. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes [J]. IEEE Trans Med Imaging, 2018, 37(12): 2663-2674.
|
| [14] |
Li W. Automatic segmentation of liver tumor in CT images with deep convolutional neural networks [J]. Journal of Computer and Communications, 2015, 3(11): 146.
|
| [15] |
Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge [J]. Contemp Oncol (Pozn), 2015, 19(1A): A68-A77.
|
| [16] |
Zhu Y, Qiu P, Ji Y. TCGA-assembler: open-source software for retrieving and processing TCGA data [J]. Nat Methods, 2014, 11(6): 599-600.
|
| [17] |
Litjens G, Bandi P, Ehteshami Bejnordi B, et al. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset [J]. Gigascience, 2018, 7(6): giy065.
|
| [18] |
Cruz-Roa A, Gilmore H, Basavanhally A, et al. Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent [J]. Sci Rep, 2017, 7: 46450.
|
| [19] |
Tellez D, Balkenhol M, Otte-Höller I, et al. Whole-slide mitosis detection in H&E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks [J]. IEEE transactions on medical imaging, 2018, 37(9): 2126-2136.
|
| [20] |
Ehteshami Bejnordi B, Mullooly M, Pfeiffer RM, et al. Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies [J]. Mod Pathol, 2018, 31(10): 1502-1512.
|
| [21] |
Steiner DF, MacDonald R, Liu Y, et al. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer [J]. Am J Surg Pathol, 2018, 42(12): 1636-1646.
|
| [22] |
Mårtensson G, Ferreira D, Granberg T, et al. The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study [J]. Medical Image Analysis, 2020, 101714.
|
| [23] |
Van Leemput K, Maes F, Vandermeulen D, et al. Automated model-based bias field correction of MR images of the brain [J]. IEEE Trans Med Imaging, 1999, 18(10): 885-896.
|
| [24] |
Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction [J]. IEEE Trans Med Imaging, 2010, 29(6): 1310-1320.
|
| [25] |
Dong P, Brankov JG, Galatsanos NP, et al. Digital watermarking robust to geometric distortions [J]. IEEE Trans Image Process, 2005, 14(12): 2140-2150.
|
| [26] |
Pluim JP, Maintz JB, Viergever MA. Mutual-information-based registration of medical images: a survey [J]. IEEE Trans Med Imaging, 2003, 22(8): 986-1004.
|
| [27] |
Schroeder W, Ng L, Cates J. The ITK software guide second edition updated for ITK version 2.4 [J]. FEBS Lett, 2005, 525: 53-58.
|
| [28] |
Klein S, Staring M, Murphy K, et al. Elastix: a toolbox for intensity-based medical image registration [J]. IEEE Trans Med Imaging, 2010, 29(1): 196-205.
|
| [29] |
Fischl B. Free Surfer [J]. Neuroimage, 2012, 62(2): 774-781.
|
| [30] |
Jenkinson M, Beckmann CF, Behrens TE, et al. FSL [J]. Neuroimage, 2012, 62(2): 782-790.
|
| [31] |
Kazemi K, Noorizadeh N. Quantitative Comparison of SPM, FSL, and Brain suite for Brain MR Image Segmentation [J]. J Biomed Phys Eng, 2014, 4(1): 13-26.
|
| [32] |
Aljabar P, Heckemann RA, Hammers A, et al. Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy [J]. Neuroimage, 2009, 46(3): 726-738.
|