Fetching the paper…
Reading the bibliography…
Remarkable successes were made in Medical Image Classification (MIC) recently, mainly due to wide applications of convolutional neural networks (CNNs).
C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning
1995
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
A dataset for breast cancer histopathological image classification
Fabio A Spanhol, Luiz S Oliveira, Caroline Petitjean, and Laurent Heutte · 2015
Earlier work this paper cites.
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, “Lung pattern classification for interstitial lung diseases using a deep convolutional neural network,” IEEE transactions on medical imaging
2016
Earlier work this paper cites.
S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmentation using convolutional neural networks in mri images,” IEEE transactions on medical imaging
2016
Earlier work this paper cites.
M. Zorzi, “Robust kalman filtering under model perturbations,” IEEE Transactions on Automatic Control
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision
2017
Earlier work this paper cites.
F. Mahmood, R. Chen, and N. J. Durr, “Unsupervised reverse domain adaptation for synthetic medical images via adversarial training,” IEEE transactions on medical imaging
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
P. Tschandl, C. Rosendahl, and H. Kittler, “The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Scientific data
2018
Cited alongside, same era.
S. Tang, X. Huang, M. Chen, C. Sun, and J. Yang, “Adversarial attack type i: Cheat classifiers by significant changes,” IEEE transactions on pattern analysis and machine intelligence
2019
Cited alongside, same era.
2019
Cited alongside, same era.
P. Yao, S. Shen, M. Xu, P. Liu, F. Zhang, J. Xing, P. Shao, B. Kaffenberger, and R. X. Xu, “Single model deep learning on imbalanced small datasets for skin lesion classification,” IEEE transactions on medical imaging
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Cui, S. Liu, L. Wang, and J. Jia, “Learnable boundary guided adversarial training,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2021
Later among the works it cites.
X. Wang, Y. Chen, and W. Zhu, “A survey on curriculum learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
S. Bai, Y. Li, Y. Zhou, Q. Li, and P. H. Torr, “Adversarial metric attack and defense for person re-identification,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2020
Cited alongside, same era.
2020
Cited alongside, same era.
A. Mustafa, S. H. Khan, M. Hayat, R. Goecke, J. Shen, and L. Shao, “Deeply supervised discriminative learning for adversarial defense,” IEEE transactions on pattern analysis and machine intelligence
2020
Cited alongside, same era.
Q. Yao, Z. He, H. Han, and S. K. Zhou, “Miss the point: Targeted adversarial attack on multiple landmark detection,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
2020
Cited alongside, same era.
S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. Van Ginneken, A. Madabhushi, J. L. Prince, D. Rueckert, and R. M. Summers, “A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises,” Proceedings of the IEEE
2021
Cited alongside, same era.
S. K. Datta, M. A. Shaikh, S. N. Srihari, and M. Gao, “Soft attention improves skin cancer classification performance,” in Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data
2021
Cited alongside, same era.
I. A. Bratchenko, L. A. Bratchenko, Y. A. Khristoforova, A. A. Moryatov, S. V. Kozlov, and V. P. Zakharov, “Classification of skin cancer using convolutional neural networks analysis of raman spectra,” Computer Methods and Programs in Biomedicine
2022
Later among the works it cites.
C. Yoo, X. Liu, F. Xing, G. El Fakhri, J. Woo, and J.-W. Kang, “Noise-robust sleep staging via adversarial training with an auxiliary model,” IEEE Transactions on Biomedical Engineering
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Yang and C. Xu, “One size does not fit all: Data-adaptive adversarial training,” in European Conference on Computer Vision
2022
Later among the works it cites.
T. Lei, D. Zhang, X. Du, X. Wang, Y. Wan, and A. K. Nandi, “Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network,” IEEE Transactions on Medical Imaging
2022
Later among the works it cites.
A. Salih, I. Boscolo Galazzo, P. Gkontra, A. M. Lee, K. Lekadir, Z. Raisi-Estabragh, and S. E. Petersen, “Explainable artificial intelligence and cardiac imaging: Toward more interpretable models,” Circulation: Cardiovascular Imaging
2023
Later among the works it cites.