Fetching the paper…
Reading the bibliography…
High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinical work.
J. Lee, K. H. Jin, Local texture estimator for implicit representation function, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 1929–1938
1938
Earlier work this paper cites.
J. Canny, A computational approach to edge detection, IEEE Transactions on pattern analysis and machine intelligence (6) (1986) 679–698
1986
Earlier work this paper cites.
R. S. Weinstein, M. R. Descour, C. Liang, G. Barker, K. M. Scott, L. Richter, E. A. Krupinski, A. K. Bhattacharyya, J. R. Davis, A. R. Graham, et al., An array microscope for ultrarapid virtual slide processing and telepathology. design, fabrication, and validation study, Human pathology 35 (11) (2004) 1303–1314
2004
Earlier work this paper cites.
J. R. Gilbertson, J. Ho, L. Anthony, D. M. Jukic, Y. Yagi, A. V. Parwani, Primary histologic diagnosis using automated whole slide imaging: a validation study, BMC clinical pathology 6 (2006) 1–19
2006
Earlier work this paper cites.
P. S. Nielsen, J. Lindebjerg, J. Rasmussen, H. Starklint, M. Waldstrøm, B. Nielsen, Virtual microscopy: an evaluation of its validity and diagnostic performance in routine histologic diagnosis of skin tumors, Human pathology 41 (12) (2010) 1770–1776
2010
Earlier work this paper cites.
L. Pantanowitz, P. N. Valenstein, A. J. Evans, K. J. Kaplan, J. D. Pfeifer, D. C. Wilbur, L. C. Collins, T. J. Colgan, Review of the current state of whole slide imaging in pathology, Journal of pathology informatics 2 (1) (2011) 36
2011
Earlier work this paper cites.
D. C. Wilbur, Digital cytology: current state of the art and prospects for the future, Acta cytologica 55 (3) (2011) 227–238
2011
Earlier work this paper cites.
F. Ghaznavi, A. Evans, A. Madabhushi, M. Feldman, Digital imaging in pathology: whole-slide imaging and beyond, Annual Review of Pathology: Mechanisms of Disease 8 (2013) 331–359
2013
Earlier work this paper cites.
C. Dong, C. C. Loy, K. He, X. Tang, Learning a deep convolutional network for image super-resolution, in: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13, Springer, 2014, pp. 184–199
2014
Earlier work this paper cites.
C. R. Drifka, J. Tod, A. G. Loeffler, Y. Liu, G. J. Thomas, K. W. Eliceiri, W. J. Kao, Periductal stromal collagen topology of pancreatic ductal adenocarcinoma differs from that of normal and chronic pancreatitis, Modern Pathology 28 (11) (2015) 1470–1480
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
A. Madabhushi, G. Lee, Image analysis and machine learning in digital pathology: Challenges and opportunities, Medical image analysis 33 (2016) 170–175
2016
Earlier work this paper cites.
J. Kim, J. K. Lee, K. M. Lee, Accurate image super-resolution using very deep convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1646–1654
2016
Earlier work this paper cites.
Y. Chen, T. Pock, Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration, IEEE transactions on pattern analysis and machine intelligence 39 (6) (2016) 1256–1272
2016
Earlier work this paper cites.
C. R. Drifka, A. G. Loeffler, K. Mathewson, A. Keikhosravi, J. C. Eickhoff, Y. Liu, S. M. Weber, W. J. Kao, K. W. Eliceiri, Highly aligned stromal collagen is a negative prognostic factor following pancreatic ductal adenocarcinoma resection, Oncotarget 7 (46) (2016) 76197
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778
2016
Earlier work this paper cites.
B. Lim, S. Son, H. Kim, S. Nah, K. Mu Lee, Enhanced deep residual networks for single image super-resolution, in: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 136–144
2017
Cited alongside, same era.
L. Cavigelli, P. Hager, L. Benini, Cas-cnn: A deep convolutional neural network for image compression artifact suppression, in: 2017 International Joint Conference on Neural Networks (IJCNN), IEEE, 2017, pp. 752–759
2017
Cited alongside, same era.
K. Sirinukunwattana, J. P. Pluim, H. Chen, X. Qi, P.-A. Heng, Y. B. Guo, L. Y. Wang, B. J. Matuszewski, E. Bruni, U. Sanchez, et al., Gland segmentation in colon histology images: The glas challenge contest, Medical image analysis 35 (2017) 489–502
2017
Cited alongside, same era.
L. Mukherjee, A. Keikhosravi, D. Bui, K. W. Eliceiri, Convolutional neural networks for whole slide image superresolution, Biomedical optics express 9 (11) (2018) 5368–5386
2018
Cited alongside, same era.
B. Niu, W. Wen, W. Ren, X. Zhang, L. Yang, S. Wang, K. Zhang, X. Cao, H. Shen, Single image super-resolution via a holistic attention network, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16, Springer, 2020, pp. 191–207
2020
Later among the works it cites.
B. Li, A. Keikhosravi, A. G. Loeffler, K. W. Eliceiri, Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization, Medical Image Analysis 68 (2021) 101938
2021
Later among the works it cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, R. Ng, Nerf: Representing scenes as neural radiance fields for view synthesis, Communications of the ACM 65 (1) (2021) 99–106
2021
Later among the works it cites.
Y. Chen, S. Liu, X. Wang, Learning continuous image representation with local implicit image function, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 8628–8638
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image super-resolution, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 2472–2481
2018
Cited alongside, same era.
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, Y. Fu, Image super-resolution using very deep residual channel attention networks, in: Proceedings of the European conference on computer vision (ECCV), 2018, pp. 286–301
2018
Cited alongside, same era.
X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, C. Change Loy, Esrgan: Enhanced super-resolution generative adversarial networks, in: Proceedings of the European conference on computer vision (ECCV) workshops, 2018, pp. 0–0
2018
Cited alongside, same era.
D. Liu, B. Wen, Y. Fan, C. C. Loy, T. S. Huang, Non-local recurrent network for image restoration, Advances in neural information processing systems 31 (2018)
2018
Cited alongside, same era.
B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, M. Welling, Rotation equivariant cnns for digital pathology, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11, Springer, 2018, pp. 210–218
2018
Cited alongside, same era.
U. Upadhyay, S. P. Awate, A mixed-supervision multilevel gan framework for image quality enhancement, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2019, pp. 556–564
2019
Cited alongside, same era.
Z. Zhang, Z. Wang, Z. Lin, H. Qi, Image super-resolution by neural texture transfer, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 7982–7991
2019
Cited alongside, same era.
Z. Chen, X. Guo, C. Yang, B. Ibragimov, Y. Yuan, Joint spatial-wavelet dual-stream network for super-resolution, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V 23, Springer, 2020, pp. 184–193
2020
Cited alongside, same era.
2021
Later among the works it cites.
F. Shahidi, Breast cancer histopathology image super-resolution using wide-attention gan with improved wasserstein gradient penalty and perceptual loss, IEEE Access 9 (2021) 32795–32809
2021
Later among the works it cites.
X. Deng, Y. Zhang, M. Xu, S. Gu, Y. Duan, Deep coupled feedback network for joint exposure fusion and image super-resolution, IEEE Transactions on Image Processing 30 (2021) 3098–3112
2021
Later among the works it cites.
H. Chen, Y. Wang, T. Guo, C. Xu, Y. Deng, Z. Liu, S. Ma, C. Xu, C. Xu, W. Gao, Pre-trained image processing transformer, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 12299–12310
2021
Later among the works it cites.
J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, R. Timofte, Swinir: Image restoration using swin transformer, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 1833–1844
2021
Later among the works it cites.
Y. Mei, Y. Fan, Y. Zhou, Image super-resolution with non-local sparse attention, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 3517–3526
2021
Later among the works it cites.
J. Ma, S. Liu, S. Cheng, R. Chen, X. Liu, L. Chen, S. Zeng, Stsrnet: Self-texture transfer super-resolution and refocusing network, IEEE Transactions on Medical Imaging 41 (2) (2021) 383–393
2021
Later among the works it cites.
C.-M. Feng, Y. Yan, H. Fu, L. Chen, Y. Xu, Task transformer network for joint mri reconstruction and super-resolution, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24, Springer, 2021, pp. 307–317
2021
Later among the works it cites.
B. Li, Y. Li, K. W. Eliceiri, Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 14318–14328
2021
Later among the works it cites.
X. Wu, Z. Chen, C. Peng, X. Ye, Mmsrnet: Pathological image super-resolution by multi-task and multi-scale learning, Biomedical Signal Processing and Control 81 (2023) 104428
2023
Later among the works it cites.
A. Juhong, B. Li, C.-Y. Yao, C.-W. Yang, D. W. Agnew, Y. L. Lei, X. Huang, W. Piyawattanametha, Z. Qiu, Super-resolution and segmentation deep learning for breast cancer histopathology image analysis, Biomedical Optics Express 14 (1) (2023) 18–36
2023
Later among the works it cites.
X. Chen, X. Wang, J. Zhou, Y. Qiao, C. Dong, Activating more pixels in image super-resolution transformer, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 22367–22377
2023
Later among the works it cites.