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Deep learning in digital pathology image analysis: a survey |
Shujian Deng1,2,3, Xin Zhang1,2,3, Wen Yan1,2,3, Eric I-Chao Chang4, Yubo Fan1,2,3, Maode Lai5, Yan Xu1,2,3,4() |
1. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China 2. Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China 3. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100191, China 4. Microsoft Research Asia, Beijing 100080, China 5. Department of Pathology, School of Medicine, Zhejiang University, Hangzhou 310007, China |
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Abstract deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.
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Keywords
pathology
deep learning
segmentation
detection
classification
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Corresponding Author(s):
Yan Xu
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Just Accepted Date: 28 June 2020
Online First Date: 27 July 2020
Issue Date: 26 August 2020
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1 |
A Cardesa, N Zidar, L Alos, A Nadal, N Gale, G Klöppel. The Kaiser’s cancer revisited: was Virchow totally wrong? Virchows Arch 2011; 458(6): 649–657
https://doi.org/10.1007/s00428-011-1075-0
pmid: 21494762
|
2 |
MN Gurcan, LE Boucheron, A Can, A Madabhushi, NM Rajpoot, B Yener. Histopathological image analysis: a review. IEEE Rev Biomed Eng 2009; 2: 147–171
https://doi.org/10.1109/RBME.2009.2034865
pmid: 20671804
|
3 |
A Andrion, C Magnani, PG Betta, A Donna, F Mollo, M Scelsi, P Bernardi, M Botta, B Terracini. Malignant mesothelioma of the pleura: interobserver variability. J Clin Pathol 1995; 48(9): 856–860
https://doi.org/10.1136/jcp.48.9.856
pmid: 7490321
|
4 |
WH Wolberg, WN Street, DM Heisey, OL Mangasarian. Computer-derived nuclear features distinguish malignant from benign breast cytology. Hum Pathol 1995; 26(7): 792–796
https://doi.org/10.1016/0046-8177(95)90229-5
pmid: 7628853
|
5 |
JP Thiran, B Macq. Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Trans Biomed Eng 1996; 43(10): 1011–1020
https://doi.org/10.1109/10.536902
pmid: 9214818
|
6 |
HK Choi, T Jarkrans, E Bengtsson, J Vasko, K Wester, PU Malmström, C Busch. Image analysis based grading of bladder carcinoma. Comparison of object, texture and graph based methods and their reproducibility. Anal Cell Pathol 1997; 15(1): 1–18
https://doi.org/10.1155/1997/147187
pmid: 9373709
|
7 |
PW Hamilton, PH Bartels, D Thompson, NH Anderson, R Montironi, JM Sloan. Automated location of dysplastic fields in colorectal histology using image texture analysis. J Pathol 1997; 182(1): 68–75
https://doi.org/10.1002/(SICI)1096-9896(199705)182:1<68::AID-PATH811>3.0.CO;2-N
|
8 |
AN Esgiar, RN Naguib, BS Sharif, MK Bennett, A Murray. Fractal analysis in the detection of colonic cancer images. IEEE Trans Inf Technol Biomed 2002; 6(1): 54–58
https://doi.org/10.1109/4233.992163
pmid: 11936597
|
9 |
P Spyridonos, P Ravazoula, D Cavouras, K Berberidis, G Nikiforidis. Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas. Med Inform Internet Med 2001; 26(3): 179–190
https://doi.org/10.1080/14639230110065757
pmid: 11706928
|
10 |
M Wiltgen, A Gerger, J Smolle. Tissue counter analysis of benign common nevi and malignant melanoma. Int J Med Inform 2003; 69(1): 17–28
https://doi.org/10.1016/S1386-5056(02)00049-7
pmid: 12485701
|
11 |
B Nielsen, F Albregtsen, HE Danielsen. The use of fractal features from the periphery of cell nuclei as a classification tool. Anal Cell Pathol 1999; 19(1): 21–37
https://doi.org/10.1155/1999/986086
pmid: 10661622
|
12 |
AN Esgiar, RN Naguib, BS Sharif, MK Bennett, A Murray. Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa. IEEE Trans Inf Technol Biomed 1998; 2(3): 197–203
https://doi.org/10.1109/4233.735785
pmid: 10719530
|
13 |
B Weyn, G van de Wouwer, S Kumar-Singh, A van Daele, P Scheunders, E van Marck, W Jacob. Computer-assisted differential diagnosis of malignant mesothelioma based on syntactic structure analysis. Cytometry 1999; 35(1): 23–29
https://doi.org/10.1002/(SICI)1097-0320(19990101)35:1<23::AID-CYTO4>3.0.CO;2-P
pmid: 10554177
|
14 |
SJ Keenan, J Diamond, WG McCluggage, H Bharucha, D Thompson, PH Bartels, PW Hamilton. An automated machine vision system for the histological grading of cervical intraepithelial neoplasia (CIN). J Pathol 2000; 192(3): 351–362
https://doi.org/10.1002/1096-9896(2000)9999:9999<::AID-PATH708>3.0.CO;2-I
pmid: 11054719
|
15 |
C Demir, SH Gultekin, B Yener. Learning the topological properties of brain tumors. IEEE/ACM Trans Comput Biol Bioinformatics 2005; 2(3): 262–270
https://doi.org/10.1109/TCBB.2005.42
pmid: 17044189
|
16 |
C Gunduz-Demir. Mathematical modeling of the malignancy of cancer using graph evolution. Math Biosci 2007; 209(2): 514–527
https://doi.org/10.1016/j.mbs.2007.03.005
pmid: 17462676
|
17 |
RS Weinstein, AR Graham, LC Richter, GP Barker, EA Krupinski, AM Lopez, KA Erps, AK Bhattacharyya, Y Yagi, JR Gilbertson. Overview of telepathology, virtual microscopy, and whole slide imaging: prospects for the future. Hum Pathol 2009; 40(8): 1057–1069
https://doi.org/10.1016/j.humpath.2009.04.006
pmid: 19552937
|
18 |
EA Krupinski, AK Bhattacharyya, RS Weinstein. Telepathology and Digital Pathology Research. Springer, Cham. 2016: 41–54
|
19 |
N Farahani, AV Parwani, L Pantanowitz. Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol Lab Med Int 2015; 7: 23–33
|
20 |
X Ying, TM Monticello. Modern imaging technologies in toxicologic pathology: an overview. Toxicol Pathol 2006; 34(7): 815–826
https://doi.org/10.1080/01926230600918983
pmid: 17178685
|
21 |
Y LeCun, Y Bengio, G Hinton. Deep learning. Nature 2015; 521(7553): 436–444
https://doi.org/10.1038/nature14539
pmid: 26017442
|
22 |
Y LeCun, BE Boser, JS Denker, D Henderson, RE Howard, WE Hubbard, LD Jackel. Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems. 1990: 396–404
|
23 |
Y LeCun, L Bottou, Y Bengio, P Haffner. Gradient-based learning applied to document recognition. Proc IEEE 1998; 86(11): 2278–2324
https://doi.org/10.1109/5.726791
|
24 |
P Wang, R Ge, X Xiao, Y Cai, G Wang, F Zhou. Rectified-linear-unit-based deep learning for biomedical multi-label data. Interdiscip Sci 2017; 9(3): 419–422
https://doi.org/10.1007/s12539-016-0196-1
|
25 |
A Madabhushi, G Lee. Image analysis and machine learning in digital pathology: challenges and opportunities. Med Image Anal 2016; 33: 170–175
https://doi.org/10.1016/j.media.2016.06.037
|
26 |
JI Epstein. An update of the Gleason grading system. J Urol 2010; 183(2): 433–440
https://doi.org/10.1016/j.juro.2009.10.046
pmid: 20006878
|
27 |
HF Frierson Jr, RA Wolber, KW Berean, DW Franquemont, MJ Gaffey, JC Boyd, DC Wilbur. Interobserver reproducibility of the Nottingham modification of the Bloom and Richardson histologic grading scheme for infiltrating ductal carcinoma. Am J Clin Pathol 1995; 103(2): 195–198
https://doi.org/10.1093/ajcp/103.2.195
pmid: 7856562
|
28 |
AM Khan, N Rajpoot, D Treanor, D Magee. A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution. IEEE Trans Biomed Eng 2014; 61(6): 1729–1738
https://doi.org/10.1109/TBME.2014.2303294
pmid: 24845283
|
29 |
A Vahadane, T Peng, A Sethi, S Albarqouni, L Wang, M Baust, K Steiger, AM Schlitter, I Esposito, N Navab. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging 2016; 35(8): 1962–1971
https://doi.org/10.1109/TMI.2016.2529665
pmid: 27164577
|
30 |
A Janowczyk, A Basavanhally, A Madabhushi. Stain normalization using Sparse AutoEncoders (StaNoSA): application to digital pathology. Comput Med Imaging Graph 2017; 57: 50–61
https://doi.org/10.1016/j.compmedimag.2016.05.003
pmid: 27373749
|
31 |
A Bentaieb, G Hamarneh. Adversarial stain transfer for histopathology image analysis. IEEE Trans Med Imaging 2018; 37(3): 792–802
https://doi.org/10.1109/TMI.2017.2781228
|
32 |
S Roy, A Kumar Jain, S Lal, J Kini. A study about color normalization methods for histopathology images. Micron 2018; 114: 42–61
https://doi.org/10.1016/j.micron.2018.07.005
pmid: 30096632
|
33 |
CW Elston, IO Ellis. Author Commentary: “Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. C. W. Elston & I. O. Ellis. Histopathology 1991; 19; 403–410.” Histopathology 2002; 41(3A): 151
https://doi.org/10.1046/j.1365-2559.2002.14691.x
pmid: 12405945
|
34 |
KH Chow, RE Factor, KS Ullman. The nuclear envelope environment and its cancer connections. Nat Rev Cancer 2012; 12(3): 196–209
https://doi.org/10.1038/nrc3219
pmid: 22337151
|
35 |
P Dey. Cancer nucleus: morphology and beyond. Diagn Cytopathol 2010; 38(5): 382–390
pmid: 19894267
|
36 |
H Chen, X Wang, PA Heng. Automated mitosis detection with deep regression networks. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE. 2016: 1204–1207
|
37 |
D Cireşan, A Giusti, LM Gambardella, J Schmidhuber. Deep neural networks segment neuronal membranes in electron microscopy images. Adv Neural Inf Process Syst 2012: 2843–2851
|
38 |
O Ronneberger, P Fischer, T Brox. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham. 2015: 234–241
|
39 |
Y Zhou, H Chang, KE Barner, B Parvin. Nuclei segmentation via sparsity constrained convolutional regression. Proc IEEE Int Symp Biomed Imaging 2015; 2015: 1284–1287
https://doi.org/10.1109/ISBI.2015.7164109
|
40 |
Y Song, L Zhang, S Chen, D Ni, B Lei, T Wang. Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning. IEEE Trans Biomed Eng 2015; 62(10): 2421–2433
https://doi.org/10.1109/TBME.2015.2430895
pmid: 25966470
|
41 |
F Xing, Y Xie, L Yang. An automatic learning-based framework for robust nucleus segmentation. IEEE Trans Med Imaging 2016; 35(2): 550–566
https://doi.org/10.1109/TMI.2015.2481436
pmid: 26415167
|
42 |
X Pan, L Li, H Yang, Z Liu, J Yang, L Zhao, Y Fan. Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks. Neurocomputing 2016; 229: S0925231216313765
|
43 |
L Zhang, M Sonka, L Lu, RM Summers, J Yao. Combining fully convolutional networks and graph-based approach for automated segmentation of cervical cell nuclei. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE. 2017: 406–409
|
44 |
MZ Alom, C Yakopcic, TM Taha, VK Asari. Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net). NAECON 2018-IEEE National Aerospace and Electronics Conference. IEEE. 2018: 228–233
|
45 |
Y Xie, F Xing, X Kong, H Su, L Yang. Beyond classification: structured regression for robust cell detection using convolutional neural network. Med Image Comput Comput Assist Interv 2015; 9351: 358–365
https://doi.org/10.1007/978-3-319-24574-4_43
|
46 |
K Sirinukunwattana, SE Ahmed Raza, YW Tsang, DRJ Snead, IA Cree, NM Rajpoot. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 2016; 35(5): 1196–1206
https://doi.org/10.1109/TMI.2016.2525803
pmid: 26863654
|
47 |
J Xu, L Xiang, Q Liu, H Gilmore, J Wu, J Tang, A Madabhushi. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 2016; 35(1): 119–130
https://doi.org/10.1109/TMI.2015.2458702
pmid: 26208307
|
48 |
Y Xie, F Xing, X Shi, X Kong, H Su, L Yang. Efficient and robust cell detection: a structured regression approach. Med Image Anal 2018; 44: 245–254
https://doi.org/10.1016/j.media.2017.07.003
pmid: 28797548
|
49 |
DC Cireşan, A Giusti, LM Gambardella, J Schmidhuber. Mitosis detection in breast cancer histology images with deep neural networks. Med Image Comput Comput Assist Interv 2013; 16(Pt 2): 411–418
https://doi.org/10.1007/978-3-642-40763-5_51
|
50 |
H Wang, A Cruz-Roa, A Basavanhally, H Gilmore, N Shih, M Feldman, J Tomaszewski, F Gonzalez, A Madabhushi. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging (Bellingham) 2014; 1(3): 034003
https://doi.org/10.1117/1.JMI.1.3.034003
pmid: 26158062
|
51 |
H Chen, Q Dou, X Wang, J Qin, PA Heng. Mitosis detection in breast cancer histology images via deep cascaded networks. Thirtieth AAAI Conference on Artificial Intelligence. 2016
|
52 |
S Albarqouni, C Baur, F Achilles, V Belagiannis, S Demirci, N Navab. Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 2016; 35(5): 1313–1321
https://doi.org/10.1109/TMI.2016.2528120
pmid: 26891484
|
53 |
C Li, X Wang, W Liu, LJ Latecki. DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med Image Anal 2018; 45: 121–133
https://doi.org/10.1016/j.media.2017.12.002
pmid: 29455111
|
54 |
M Ma, Y Shi, W Li, Y Gao, J Xu. A novel two-stage deep method for mitosis detection in breast cancer histology images. 2018 24th International Conference on Pattern Recognition (ICPR). IEEE. 2018: 3892–3897
|
55 |
C Li, X Wang, W Liu, LJ Latecki, B Wang, J Huang. Weakly supervised mitosis detection in breast histopathology images using concentric loss. Med Image Anal 2019; 53: 165–178
https://doi.org/10.1016/j.media.2019.01.013
pmid: 30798116
|
56 |
L Yang, Y Zhang, IH Guldner, S Zhang, DZ Chen. 3D segmentation of glial cells using fully convolutional networks and k-terminal cut. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham. 2016: 658–666
|
57 |
SU Akram, J Kannala, L Eklund, J Heikkilä. Cell proposal network for microscopy image analysis. 2016 IEEE International Conference on Image Processing (ICIP). IEEE. 2016: 3199–3203
|
58 |
SU Akram, J Kannala, L Eklund, J Heikkilä. Cell segmentation proposal network for microscopy image analysis. Deep Learning and Data Labeling for Medical Applications. Springer, Cham. 2016: 21–29
|
59 |
H Chen, X Qi, L Yu, Q Dou, J Qin, PA Heng. DCAN: deep contour-aware networks for object instance segmentation from histology images. Med Image Anal 2017; 36: 135–146
https://doi.org/10.1016/j.media.2016.11.004
|
60 |
Y Song, EL Tan, X Jiang, JZ Cheng, D Ni, S Chen, B Lei, T Wang. Accurate cervical cell segmentation from overlapping clumps in pap smear images. IEEE Trans Med Imaging 2017; 36(1): 288–300
https://doi.org/10.1109/TMI.2016.2606380
pmid: 27623573
|
61 |
N Kumar, R Verma, S Sharma, S Bhargava, A Vahadane, A Sethi. A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans Med Imaging 2017; 36(7): 1550–1560
https://doi.org/10.1109/TMI.2017.2677499
pmid: 28287963
|
62 |
DJ Ho, C Fu, P Salama, KW Dunn, EJ Delp. Nuclei detection and segmentation of fluorescence microscopy images using three dimensional convolutional neural networks. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE. 2018: 418–422
|
63 |
P Naylor, M Laé, F Reyal, T Walter. Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans Med Imaging 2019; 38(2): 448–459
https://doi.org/10.1109/TMI.2018.2865709
pmid: 30716022
|
64 |
Y Zhou, OF Onder, Q Dou, E Tsougenis, H Chen, PA Heng. Cia-net: Robust nuclei instance segmentation with contour-aware information aggregation. International Conference on Information Processing in Medical Imaging. Springer, Cham. 2019: 682–693
|
65 |
I Arganda-Carreras, HS Seung, A Cardona, J Schindelin. Segmentation of neuronal structures in EM stacks challenge–ISBI 2012. 2012
|
66 |
A Oren, J Fernandes. The Bethesda system for the reporting of cervical/vaginal cytology. J Am Osteopath Assoc 1991; 91(5): 476–479
|
67 |
S Naik, S Doyle, M Feldman, J Tomaszewski, A Madabhushi. Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. MIAAB workshop. 2007: 1–8
|
68 |
PS Karvelis, DI Fotiadis, I Georgiou, M Syrrou. A watershed based segmentation method for multispectral chromosome images classification. Conf Proc IEEE Eng Med Biol Soc 2006; 2006: 3009–3012
https://doi.org/10.1109/IEMBS.2006.260682
|
69 |
S Petushi, FU Garcia, MM Haber, C Katsinis, A Tozeren. Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Med Imaging 2006; 6(1): 14
https://doi.org/10.1186/1471-2342-6-14
pmid: 17069651
|
70 |
E Shelhamer, J Long, T Darrell. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017; 39(4): 640–651
https://doi.org/10.1109/TPAMI.2016.2572683
|
71 |
O Oktay, J Schlemper, LL Folgoc, M Lee, M Heinrich, K Misawa, K Mori, S McDonagh, NY Hammerla, B Kainz, B Glocker, D Rueckert. Attention U-net: learning where to look for the pancreas. arXiv 2018; 1804.03999
|
72 |
Z Zeng, W Xie, Y Zhang, Y. Lu RIC-Unet: an improved neural network based on Unet for nuclei segmentation in histology images. IEEE Access 2019; 7: 21420–21428
|
73 |
JM Chen, Y Li, J Xu, L Gong, LW Wang, WL Liu, J Liu. Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: a review. Tumour Biol 2017; 39(3): 1010428317694550
https://doi.org/10.1177/1010428317694550
pmid: 28347240
|
74 |
M Veta, JP Pluim, PJ van Diest, MA Viergever. Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng 2014; 61(5): 1400–1411
https://doi.org/10.1109/TBME.2014.2303852
pmid: 24759275
|
75 |
C Sommer, L Fiaschi, FA Hamprecht, DW Gerlich. Learning-based mitotic cell detection in histopathological images. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). IEEE. 2012: 2306–2309
|
76 |
M Veta, PJ van Diest, JPW Pluim. Detecting mitotic figures in breast cancer histopathology images. Medical Imaging 2013: Digital Pathology. International Society for Optics and Photonics. 2013; 8676: 867607
|
77 |
AM Khan, H Eldaly, NM Rajpoot. A gamma-Gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. J Pathol Inform 2013; 4(4): 149–152
pmid: 23858386
|
78 |
A Paul, A Dey, DP Mukherjee, J Sivaswamy, V Tourani. Regenerative random forest with automatic feature selection to detect mitosis in histopathological breast cancer images. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham. 2015: 94–102
|
79 |
AA Cruz-Roa, JE Arevalo Ovalle, A Madabhushi, FA González Osorio. A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. Med Image Comput Comput Assist Interv 2013; 16(Pt 2): 403–410
https://doi.org/10.1007/978-3-642-40763-5_50
|
80 |
M Veta, PJ van Diest, SM Willems, H Wang, A Madabhushi, A Cruz-Roa, F Gonzalez, AB Larsen, JS Vestergaard, AB Dahl, DC Cireşan, J Schmidhuber, A Giusti, LM Gambardella, FB Tek, T Walter, CW Wang, S Kondo, BJ Matuszewski, F Precioso, V Snell, J Kittler, TE de Campos, AM Khan, NM Rajpoot, E Arkoumani, MM Lacle, MA Viergever, JP Pluim. Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal 2015; 20(1): 237–248
https://doi.org/10.1016/j.media.2014.11.010
pmid: 25547073
|
81 |
L Roux, D Racoceanu, N Loménie, M Kulikova, H Irshad, J Klossa, F Capron, C Genestie, GL Naour, MN Gurcan. Mitosis detection in breast cancer histological images. An ICPR 2012 contest. J Pathol Inform 2013; 4: 8
https://doi.org/10.4103/2153-3539.112693
|
82 |
F Yang, MA Mackey, F Ianzini, G Gallardo, M Sonka. Cell segmentation, tracking, and mitosis detection using temporal context. Med Image Comput Comput Assist Interv 2005; 8(Pt 1): 302–309
https://doi.org/doi:10.1007/11566465_38
|
83 |
C Payer, D Štern, M Feiner, H Bischof, M Urschler. Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks. Med Image Anal 2019; 57: 106–119
https://doi.org/10.1016/j.media.2019.06.015
|
84 |
B Hariharan, P Arbeláez, R Girshick, J Malik. Simultaneous detection and segmentation. European Conference on Computer Vision. Springer, Cham. 2014: 297–312
|
85 |
M Veta, PJ Van Diest, JPW Pluim. Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham. 2016: 632–639
|
86 |
P Kronqvist, T Kuopio, Y Collan. Morphometric grading of invasive ductal breast cancer. I. Thresholds for nuclear grade. Br J Cancer 1998; 78(6): 800–805
https://doi.org/10.1038/bjc.1998.582
pmid: 9743304
|
87 |
EC Mommers, DL Page, WD Dupont, P Schuyler, AM Leonhart, JP Baak, CJ Meijer, PJ van Diest. Prognostic value of morphometry in patients with normal breast tissue or usual ductal hyperplasia of the breast. Int J Cancer 2001; 95(5): 282–285
pmid: 11494225
|
88 |
M Veta, R Kornegoor, A Huisman, AH Verschuur-Maes, MA Viergever, JP Pluim, PJ van Diest. Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer. Mod Pathol 2012; 25(12): 1559–1565
https://doi.org/10.1038/modpathol.2012.126
pmid: 22899294
|
89 |
M Maška, V Ulman, D Svoboda, P Matula, P Matula, C Ederra, A Urbiola, T España, S Venkatesan, DMW Balak, P Karas, T Bolcková, M Štreitová, C Carthel, S Coraluppi, N Harder, K Rohr, KEG Magnusson, J Jaldén, HM Blau, O Dzyubachyk, P Křížek, GM Hagen, DP Escuredo, DJ Carretero, MJL Carbayo, AM Barrutia, E Meijering, M Kozubek, CO Solorzano. A benchmark for comparison of cell tracking algorithms. Bioinformatics 2014; 30(11): 1609–1617
https://doi.org/10.1093/bioinformatics/btu080
|
90 |
QD Vu, S Graham, NN To. Minh, Muhammad S, Talha Q, Navid AK, Ali KS, Tahsin K, Keyvan F, Tianhao Z, Rajarsi G, Tae KJ, Nasir R, Joel S. Methods for segmentation and classification of digital microscopy tissue images. Front Bioeng Biotechnol 2019; 7: 53
https://doi.org/10.3389/fbioe.2019.00053
|
91 |
P Naylor, M Laé, F Reyal, et al.. Nuclei segmentation in histopathology images using deep neural networks[C]//2017 IEEE 14th International Symposium On Biomedical Imaging (ISBI 2017). IEEE. 2017: 933–936
|
92 |
N Kumar, R Verma, D Anand, Y Zhou, OF Onder, E Tsougenis, H Chen, P Heng, J Li, Z Hu, Y Wang, NA Koohbanani, M Jahanifar, NZ Tajeddin, A Gooya, N Rajpoot, X Ren, S Zhou, Q Wang, D Shen, C Yang, C Weng, W Yu, C Yeh, S Yang, S Xu, PH Yeung, P Sun, A Mahbod, G Schaefer, I Ellinger, R Ecker, O Smedby, C Wang, B Chidester, T Ton, M Tran, J Ma, MN Do, S Graham, QD Vu, JT Kwak, A Gunda, R Chunduri, C Hu, X Zhou, D Lotfi, R Safdari, A Kascenas, A O’Neil, D Eschweiler, J Stegmaier, Y Cui, B Yin, K Chen, X Tian, P Gruening, E Barth, E Arbel, L Remer, A Ben-Dor, E Sirazitdinova, M Kohl, S Braunewell, Y Li, X Xie, L Shen, J Ma, KD Baksi, MA Khan, J Choo, A Colomer, V Naranjo, L Pei, KM Iftekharuddin, K Roy, D Bhattacharjee, A Pedraza, MG Bueno, S Devanathan, S Radhakrishnan, P Koduganty, Z Wu, G Cai, X Liu, Y Wang, A Sethi. A multi-organ nucleus segmentation challenge. IEEE Trans Med Imaging 2020; 39(5): 1380–1391
https://doi.org/10.1109/TMI.2019.2947628
|
93 |
A Neubeck, L Van Gool. Efficient non-maximum suppression. 18th International Conference on Pattern Recognition (ICPR'06). IEEE. 2006, 3: 850–855
|
94 |
CR Maurer, R Qi, V Raghavan. A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Mach Intell 2003; 25(2): 265–270
https://doi.org/10.1109/TPAMI.2003.1177156
|
95 |
S Ren, K He, R Girshick, J Sun. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 2017; 39(6): 1137–1149
https://doi.org/10.1109/TPAMI.2016.2577031
|
96 |
R Zhang, C Cheng, X Zhao, X Li. Multiscale mask R-CNN-based lung tumor detection using PET imaging. Mol Imaging 2019; 18: 1536012119863531
https://doi.org/10.1177/1536012119863531
|
97 |
S Wang, R Rong, DM Yang, J Fujimoto, S Yan, L Cai, L Yang, D Luo, C Behrens , ER Parra, B Yao, L Xu, T Wang, X Zhan, II Wistuba, J Minna, Y Xie, G Xiao. Computational staining of pathology images to study the tumor microenvironment in lung cancer. Cancer Res 2020; 80(10): 2056–2066
https://doi.org/10.1158/0008-5472.CAN-19-1629
pmid: 31915129
|
98 |
JW Johnson. Adapting mask-RCNN for automatic nucleus segmentation. arXiv 2018; 1805.00500
|
99 |
SA Hoda, RS Hoda. Rubin’s pathology: clinicopathologic foundations of medicine. JAMA 2007; 298(17): 2070–2075
|
100 |
K Sirinukunwattana, JPW Pluim, H Chen, X Qi, PA Heng, YB Guo, LY Wang, BJ Matuszewski, E Bruni, U Sanchez, A Böhm, O Ronneberger, BB Cheikh, D Racoceanu, P Kainz, M Pfeiffer, M Urschler, DRJ Snead, NM Rajpoot. Gland segmentation in colon histology images: the GlaS challenge contest. Med Image Anal 2017; 35: 489–502
https://doi.org/10.1016/j.media.2016.08.008
pmid: 27614792
|
101 |
C Gunduz-Demir, M Kandemir, AB Tosun, C Sokmensuer. Automatic segmentation of colon glands using object-graphs. Med Image Anal 2010; 14(1): 1–12
https://doi.org/10.1016/j.media.2009.09.001
pmid: 19819181
|
102 |
KR Hess, GR Varadhachary, SH Taylor, W Wei, MN Raber, R Lenzi, JL Abbruzzese. Metastatic patterns in adenocarcinoma. Cancer 2006; 106(7): 1624–1633
https://doi.org/10.1002/cncr.21778
pmid: 16518827
|
103 |
DP Ryan, TS Hong, N Bardeesy. Pancreatic adenocarcinoma. N Engl J Med 2014; 371(11): 1039–1049
https://doi.org/10.1056/NEJMra1404198
pmid: 25207767
|
104 |
M Fleming, S Ravula, SF Tatishchev, HL Wang. Colorectal carcinoma: pathologic aspects. J Gastrointest Oncol 2012; 3(3): 153–173
pmid: 22943008
|
105 |
WD Travis, E Brambilla, KR Geisinger. Histological grading in lung cancer: one system for all or separate systems for each histological type? Eur Respir J 2016; 47(3): 720–723
https://doi.org/10.1183/13993003.00035-2016
|
106 |
Y Xu, Y Li, M Liu, Y Wang, M Lai, C Eric. Gland instance segmentation by deep multichannel side supervision. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham. 2016: 496–504
|
107 |
P Kainz, M Pfeiffer, M Urschler. Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization. PeerJ 2017; 5: e3874
https://doi.org/10.7717/peerj.3874
|
108 |
W Li, S Manivannan, S Akbar, J Zhang, E Trucco, SJ McKenna. Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE. 2016: 1405–1408
|
109 |
Y Xu, Y Li, Y Wang, M Liu, Y Fan, M Lai, EIC Chang. Gland instance segmentation using deep multichannel neural networks. IEEE Trans Biomed Eng 2017; 64(12): 2901–2912
https://doi.org/10.1109/TBME.2017.2686418
pmid: 28358671
|
110 |
A BenTaieb, G Hamarneh. Topology aware fully convolutional networks for histology gland segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham. 2016: 460–468
|
111 |
A BenTaieb, J Kawahara, G Hamarneh. Multi-loss convolutional networks for gland analysis in microscopy. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE. 2016: 642–645
|
112 |
HS Wu, R Xu, N Harpaz, D Burstein, J Gil. Segmentation of microscopic images of small intestinal glands with directional 2-D filters. Anal Quant Cytol Histol 2005; 27(5): 291–300
pmid: 16447822
|
113 |
R Farjam, H Soltanian-Zadeh, K Jafari-Khouzani, RA Zoroofi. An image analysis approach for automatic malignancy determination of prostate pathological images. Cytometry B Clin Cytom 2007; 72(4): 227–240
https://doi.org/10.1002/cyto.b.20162
pmid: 17285628
|
114 |
HS Wu, R Xu, N Harpaz, D Burstein, J Gil. Segmentation of intestinal gland images with iterative region growing. J Microsc 2005; 220(Pt 3): 190–204
https://doi.org/10.1111/j.1365-2818.2005.01531.x
pmid: 16364002
|
115 |
H Fu, G Qiu, J Shu, M Ilyas. A novel polar space random field model for the detection of glandular structures. IEEE Trans Med Imaging 2014; 33(3): 764–776
https://doi.org/10.1109/TMI.2013.2296572
pmid: 24595348
|
116 |
K Sirinukunwattana, DR Snead, NM Rajpoot. A stochastic polygons model for glandular structures in colon histology images. IEEE Trans Med Imaging 2015; 34(11): 2366–2378
https://doi.org/10.1109/TMI.2015.2433900
pmid: 25993703
|
117 |
JP Monaco, JE Tomaszewski, MD Feldman, I Hagemann, M Moradi, P Mousavi, A Boag, C Davidson, P Abolmaesumi, A Madabhushi. High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models. Med Image Anal 2010; 14(4): 617–629
https://doi.org/10.1016/j.media.2010.04.007
pmid: 20493759
|
118 |
J Diamond, NH Anderson, PH Bartels, R Montironi, PW Hamilton. The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. Hum Pathol 2004; 35(9): 1121–1131
https://doi.org/10.1016/j.humpath.2004.05.010
pmid: 15343515
|
119 |
S Doyle, A Madabhushi, M Feldman, J Tomaszeweski. A boosting cascade for automated detection of prostate cancer from digitized histology. Med Image Comput Comput Assist Interv 2006; 9(Pt 2): 504–511
|
120 |
A Tabesh, M Teverovskiy, HY Pang, VP Kumar, D Verbel, A Kotsianti, O Saidi. Multifeature prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans Med Imaging 2007; 26(10): 1366–1378
https://doi.org/10.1109/TMI.2007.898536
pmid: 17948727
|
121 |
K Nguyen, A Sarkar, AK Jain. Structure and context in prostatic gland segmentation and classification. Med Image Comput Comput Assist Interv 2012; 15(Pt 1): 115–123
https://doi.org/10.1007/978-3-642-33415-3_15
|
122 |
JG Jacobs, E Panagiotaki, DC Alexander. Gleason grading of prostate tumours with max-margin conditional random fields. International Workshop on Machine Learning in Medical Imaging. Springer, Cham. 2014: 85–92
|
123 |
B Sabata, B Babenko, R Monroe, C Srinivas. Automated analysis of pin-4 stained prostate needle biopsies. International Workshop on Prostate Cancer Imaging. Springer, Berlin, Heidelberg. 2010: 89–100
|
124 |
D Altunbay, C Cigir, C Sokmensuer, C Gunduz-Demir. Color graphs for automated cancer diagnosis and grading. IEEE Trans Biomed Eng 2010; 57(3): 665–674
https://doi.org/10.1109/TBME.2009.2033804
pmid: 19846369
|
125 |
A Fakhrzadeh, E Sporndly-Nees, L Holm, CLL Hendriks. Analyzing tubular tissue in histopathological thin sections. 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA). IEEE. 2012: 1–6
|
126 |
A Krizhevsky, I Sutskever, GE Hinton. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012: 1097–1105
|
127 |
C Szegedy, W Liu, Y Jia, P Sermanet, S Reed, D Anguelov, D Erhan, V Vanhoucke, A Rabinovich. Going deeper with convolutions. Proceedings of the IEEE Conference on computer Vision and Pattern Recognition. 2015: 1–9
|
128 |
J Deng, W Dong, R Socher, LJ Li, K Li, L Fei-Fei. Imagenet: a large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2009: 248–255
|
129 |
S Manivannan, W Li, S Akbar, R Wang, J Zhang, SJ McKenna. An automated pattern recognition system for classifying indirect immunofluorescence images of hep-2 cells and specimens. Pattern Recognit 2016; 51: 12–26
https://doi.org/10.1016/j.patcog.2015.09.015
|
130 |
Y Xu, Z Jia, LB Wang, Y Ai, F Zhang, M Lai, EIC Chang. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics 2017; 18(1): 281
https://doi.org/10.1186/s12859-017-1685-x
pmid: 28549410
|
131 |
Y Xu, T Mo, Q Feng, P Zhong, M Lai, EI Chang. Deep learning of feature representation with multiple instance learning for medical image analysis. IEEE International Conference on Acoustics, Speech and Signal Processing 2014; 1626–1630
https://doi.org/10.1109/ICASSP.2014.6853873
|
132 |
Y Xu, Z Jia, Y Ai, F Zhang, M Lai, EI Change. Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation. IEEE International Conference on Acoustics, Speech and Signal Processing. 2015: 947–951
https://doi.org/10.1109/ICASSP.2015.7178109
|
133 |
L Hou, D Samaras, TM Kurc, Y Gao, JE Davis, JH Saltz. Patch-based convolutional neural network for whole slide tissue image classification. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016; 2016: 2424–2433
https://doi.org/10.1109/CVPR.2016.266
pmid: 27795661
|
134 |
H Källén, J Molin, A Heyden, C Lundstrom, K Astrom. Towards grading gleason score using generically trained deep convolutional neural networks. IEEE International Symposium on Biomedical Imaging. 2016: 1163–1167
https://doi.org/10.1109/isbi.2016.7493473
|
135 |
J Xu, X Luo, G Wang, H Gilmore, A Madabhushi. A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 2016; 191: 214–223
https://doi.org/10.1016/j.neucom.2016.01.034
pmid: 28154470
|
136 |
T Qaiser, YW Tsang, D Epstein, N Rajpoot. Tumor segmentation in whole slide images using persistent homology and deep convolutional features. Annual Conference on Medical Image Understanding and Analysis. 2017; 723: 320–329
https://doi.org/10.1007/978-3-319-60964-5_28
|
137 |
Z Jia, X Huang, EI Chang, Y Xu. Constrained deep weak supervision for histopathology image segmentation. IEEE Trans Med Imaging 2017; 36(11): 2376–2388
https://doi.org/10.1109/TMI.2017.2724070
pmid: 28692971
|
138 |
P Courtiol, EW Tramel, M Sanselme, G Wainrib. Classification and disease localization in histopathology using only global labels: a weakly-supervised approach. 2018
|
139 |
X Wang, H Chen, C Gan, H Lin, Q Dou, E Tsougenis, Q Huang, M Cai, PA Heng. Weakly supervised deep learning for whole slide lung cancer image analysis. IEEE Trans Cybern 2019; [Epub ahead of print] doi: 10.1109/TCYB.2019.2935141
https://doi.org/10.1109/TCYB.2019.2935141
pmid: 31484154
|
140 |
C Mercan, S Aksoy, E Mercan, LG Shapiro, DL Weaver, JG Elmore. From patch-level to roi-level deep feature representations for breast histopathology classification. Medical Imaging 2019: Digital Pathology. 2019. 109560H
https://doi.org/10.1117/12.2510665
|
141 |
Y Xu, L Jiao, S Wang, J Wei, Y Fan, M Lai, EI Chang. Multi-label classification for colon cancer using histopathological images. Microsc Res Tech 2013; 76(12): 1266–1277
https://doi.org/10.1002/jemt.22294
pmid: 24123468
|
142 |
L Jiao, C Qi, S Li, X Yan. Colon cancer detection using whole slide histopathological images. IFMBE Proc 2013; 39: 1283–1286
https://doi.org/10.1007/978-3-642-29305-4_336
|
143 |
MM Adankon, M Cheriet. Support vector machine. Comput Sci 2002; 1(4): 1–28
|
144 |
P Golland, N Hata, C Barillot, J Hornegger, R Howe. Preface. Medical image computing and computer-assisted intervention—MICCAI 2014. Med Image Comput Comput Assist Interv 2014; 17(Pt 1): V–VI
|
145 |
P Sermanet, D Eigen, X Zhang, M Mathieu, R Fergus, Y Lecun. Overfeat: integrated recognition, localization and detection using convolutional networks. International Conference on Learning Representations. 2014
|
146 |
A Liaw, M Wiener. Classification and regression by random forest. R News 2007; 2: 18–22
|
147 |
K Simonyan, A Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv 2014; 1409.1556v6
|
148 |
X Yan, JY Zhu, EI Chang, Z Tu. Multiple clustered instance learning for histopathology cancer image classification, segmentation and clustering. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2012; 964–971
https://doi.org/10.1109/cvpr.2012.6247772
|
149 |
Y Xu, J Zhang, EI Chang, M Lai, Z Tu. Context-constrained multiple instance learning for histopathology image segmentation. Med Image Comput Comput Assist Interv 2012; 15(Pt 3): 623–630
https://doi.org/10.1007/978-3-642-33454-2_77
pmid: 23286183
|
150 |
Y Xu, JY Zhu, EI Chang, M Lai, Z Tu. Weakly supervised histopathology cancer image segmentation and classification. Med Image Anal 2014; 18(3): 591–604
https://doi.org/10.1016/j.media.2014.01.010
pmid: 24637156
|
151 |
Y Xu, Y Li, Z Shen, Z Wu, T Gao, Y Fan, M Lai, EI Chang. Parallel multiple instance learning for extremely large histopathology image analysis. BMC Bioinformatics 2017; 18(1): 360
https://doi.org/10.1186/s12859-017-1768-8
pmid: 28774262
|
152 |
K He, X Zhang, S Ren, J Sun. Deep residual learning for image recognition. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2016: 770–778
https://doi.org/10.1109/cvpr.2016.90
|
153 |
RG Hagerty, PN Butow, PM Ellis, S Dimitry, MH Tattersall. Communicating prognosis in cancer care: a systematic review of the literature. Ann Oncol 2005; 16(7): 1005–1053
https://doi.org/10.1093/annonc/mdi211
pmid: 15939716
|
154 |
B Norgeot, BS Glicksberg, AJ Butte. A call for deep-learning healthcare. Nat Med 2019; 25(1): 14–15
https://doi.org/10.1038/s41591-018-0320-3
pmid: 30617337
|
155 |
H Uramoto, F Tanaka. Recurrence after surgery in patients with NSCLC. Transl Lung Cancer Res 2014; 3(4): 242–249
pmid: 25806307
|
156 |
X Wang, A Janowczyk, Y Zhou, R Thawani, P Fu, K Schalper, V Velcheti, A Madabhushi. Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images. Sci Rep 2017; 7(1): 13543
https://doi.org/10.1038/s41598-017-13773-7
pmid: 29051570
|
157 |
P Vaidya, X Wang, K Bera, A Khunger, H Choi, P Patil, V Velcheti, A Madabhushi. Raptomics: integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer. Medical Imaging 2018: Digital Pathology International Society for Optics and Photonics. 2018: 105810M
https://doi.org/10.1117/12.2296646
|
158 |
M Sanchez-Cespedes, P Parrella, M Esteller, S Nomoto, B Trink, JM Engles, WH Westra, JG Herman, D Sidransky. Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung. Cancer Res 2002; 62(13): 3659–3662
pmid: 12097271
|
159 |
DB Shackelford, E Abt, L Gerken, DS Vasquez, A Seki, M Leblanc, L Wei, MC Fishbein, J Czernin, PS Mischel, RJ Shaw. LKB1 inactivation dictates therapeutic response of non-small cell lung cancer to the metabolism drug phenformin. Cancer Cell 2013; 23(2): 143–158
https://doi.org/10.1016/j.ccr.2012.12.008
pmid: 23352126
|
160 |
DV Parums. Current status of targeted therapy in non-small cell lung cancer. Drugs Today (Barc) 2014; 50(7): 503–525
https://doi.org/10.1358/dot.2014.50.07.2185913
pmid: 25101332
|
161 |
S Wang, DM Yang, R Rong, X Zhan, J Fujimoto, H Liu, J Minna, II Wistuba, Y Xie, G Xiao. Artificial intelligence in lung cancer pathology image analysis. Cancers (Basel) 2019; 11(11): 1673
https://doi.org/10.3390/cancers11111673
pmid: 31661863
|
162 |
N Coudray, PS Ocampo, T Sakellaropoulos, N Narula, M Snuderl, D Fenyö, AL Moreira, N Razavian, A Tsirigos. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med 2018; 24(10): 1559–1567
https://doi.org/10.1038/s41591-018-0177-5
pmid: 30224757
|
163 |
JN Kather, AT Pearson, N Halama, D Jäger, J Krause, SH Loosen, A Marx, P Boor, F Tacke, UP Neumann, HI Grabsch, T Yoshikawa, H Brenner, J Chang-Claude, M Hoffmeister, C Trautwein, T Luedde. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 2019; 25(7): 1054–1056
https://doi.org/10.1038/s41591-019-0462-y
pmid: 31160815
|
164 |
DT Le, JN Uram, H Wang, BR Bartlett, H Kemberling, AD Eyring, AD Skora, BS Luber, NS Azad, D Laheru, B Biedrzycki, RC Donehower, A Zaheer, GA Fisher, TS Crocenzi, JJ Lee, SM Duffy, RM Goldberg, A de la Chapelle, M Koshiji, F Bhaijee, T Huebner, RH Hruban, LD Wood, N Cuka, DM Pardoll, N Papadopoulos, KW Kinzler, S Zhou, TC Cornish, JM Taube, RA Anders, JR Eshleman, B Vogelstein, LA Diaz Jr. Pd-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 2015; 372(26): 2509–2520
https://doi.org/10.1056/NEJMoa1500596
pmid: 26028255
|
165 |
K Nagpal, D Foote, Y Liu, PHC Chen, E Wulczyn, F Tan, N Olson, JL Smith, A Mohtashamian, JH Wren, GS Corrado, R MacDonald, LH Peng, MB Amin, AJ Evans, AR Sangoi, CH Mermel, JD Hipp, MC Stumpe. Development and validation of a deep learning algorithm for improving gleason scoring of prostate cancer. NPJ Digit Med 2019; 2(1): 1–10
https://doi.org/10.1038/s41746-019-0112-2
pmid: 31304394
|
166 |
C Szegedy, V Vanhoucke, S Ioffe, J Shlens, Z Wojna. Rethinking the inception architecture for computer vision. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2016: 2818–2826
https://doi.org/10.1109/cvpr.2016.308
|
167 |
N Ing, Z Ma, J Li, H Salemi, C Arnold, BS Knudsen, A Gertych. Semantic segmentation for prostate cancer grading by convolutional neural networks. Medical Imaging 2018: Digital Pathology International Society for Optics and Photonics. 2018: 105811B
https://doi.org/10.1117/12.2293000
|
168 |
V Badrinarayanan, A Kendall, R Cipolla. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 2017; 39(12): 2481–2495
https://doi.org/10.1109/TPAMI.2016.2644615
pmid: 28060704
|
169 |
W Li, J Li, KV Sarma, KC Ho, S Shen, BS Knudsen, A Gertych, CW Arnold. Path R-CNN for prostate cancer diagnosis and gleason grading of histological images. IEEE Trans Med Imaging 2019; 38(4): 945–954
https://doi.org/10.1109/TMI.2018.2875868
pmid: 30334752
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