Fetching the paper…
Reading the bibliography…
Deep learning (DL) techniques have been extensively utilized for medical image classification.
R. M. Haralick, K. S. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern
1973
Earlier work this paper cites.
K. Kira and L. A. Rendell, “The feature selection problem: Traditional methods and a new algorithm,” in Aaai
1992
Earlier work this paper cites.
C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning
1995
Earlier work this paper cites.
M. Kubat, S. Matwin, et al
1997
Earlier work this paper cites.
X. Tang, “Texture information in run-length matrices,” IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
1998
Earlier work this paper cites.
M. A. Hall, “Correlation-based feature selection of discrete and numeric class machine learning,” 2000
2000
Earlier work this paper cites.
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on pattern analysis and machine intelligence
2002
Earlier work this paper cites.
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine learning
2002
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing
2004
Earlier work this paper cites.
H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on pattern analysis and machine intelligence
2005
Earlier work this paper cites.
M. Sasikala and N. Kumaravel, “A wavelet-based optimal texture feature set for classification of brain tumours,” Journal of medical engineering & technology
2008
Earlier work this paper cites.
Y. Mingqiang, K. Kidiyo, R. Joseph, et al
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” 2009 IEEE Conference on Computer Vision and Pattern Recognition
2009
Earlier work this paper cites.
X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics
2010
Earlier work this paper cites.
S. Wang and R. M. Summers, “Machine learning and radiology,” Medical image analysis
2012
Earlier work this paper cites.
G. Chandrashekar and F. Sahin, “A survey on feature selection methods,” Computers & Electrical Engineering
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
X. Zhou, S. Wang, W. Xu, G. Ji, P. Phillips, P. Sun, and Y. Zhang, “Detection of pathological brain in mri scanning based on wavelet-entropy and naive bayes classifier,” in IWBBIO
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015
Earlier work this paper cites.
C. Xu, C. Lu, X. Liang, J. Gao, W. Zheng, T. Wang, and S. Yan, “Multi-loss regularized deep neural network,” IEEE Transactions on Circuits and Systems for Video Technology
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR
2015
Earlier work this paper cites.
U. R. Acharya, P. Chowriappa, H. Fujita, S. Bhat, S. Dua, J. E. W. Koh, W. J. E. Lim, P. Kongmebhol, and K. H. Ng, “Thyroid lesion classification in 242 patient population using gabor transform features from high resolution ultrasound images,” Knowl. Based Syst
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
F. A. Spanhol, L. S. Oliveira, C. Petitjean, and L. Heutte, “Breast cancer histopathological image classification using convolutional neural networks,” in 2016 international joint conference on neural networks (IJCNN)
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” ArXiv
2016
Earlier work this paper cites.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” 2016
2016
Earlier work this paper cites.
A. Shrivastava, A. Gupta, and R. B. Girshick, “Training region-based object detectors with online hard example mining,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Cited alongside, same era.
A. Bentaieb, J. Kawahara, and G. Hamarneh, “Multi-loss convolutional networks for gland analysis in microscopy,” 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
2016
Cited alongside, same era.
PMID: 28301734
D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual Review of Biomedical Engineering · 2017
Cited alongside, same era.
Z. Han, B. Wei, Y. Zheng, Y. Yin, K. Li, and S. Li, “Breast cancer multi-classification from histopathological images with structured deep learning model,” Scientific Reports
2017
Cited alongside, same era.
Y. Zhan, D. Hu, Y. Wang, and X. Yu, “Semisupervised hyperspectral image classification based on generative adversarial networks,” IEEE Geoscience and Remote Sensing Letters
J. Su, “O-gan: Extremely concise approach for auto-encoding generative adversarial networks,” ArXiv
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32
2019
Later among the works it cites.
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data
2019
Later among the works it cites.
M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in International conference on machine learning
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
A. Varghese, P. MohammedSafwanK., S. Chennamsetty, and G. Krishnamurthi, “Generative adversarial networks for brain lesion detection,” in Medical Imaging
2017
Cited alongside, same era.
T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017
Cited alongside, same era.
P. Jain and P. Kar, “Non-convex optimization for machine learning,” Found. Trends Mach. Learn
2017
Cited alongside, same era.
H. Chen, Y. Zhang, W. Zhang, P. Liao, K. Li, J. Zhou, and G. Wang, “Low-dose ct via convolutional neural network,” Biomedical optics express
2017
Cited alongside, same era.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2017
Cited alongside, same era.
T.-Y. Lin, P. Goyal, R. B. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” 2017 IEEE International Conference on Computer Vision (ICCV)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019
Later among the works it cites.
Priyanka and D. Kumar, “Feature extraction and selection of kidney ultrasound images using glcm and pca,” Procedia Computer Science
2020
Later among the works it cites.
Z. Huang, S. Liang, M. Liang, and H. Yang, “Dianet: Dense-and-implicit attention network,” in Proceedings of the AAAI Conference on Artificial Intelligence
2020
Later among the works it cites.
A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-turjman, and P. Pinheiro, “Covidgan: Data augmentation using auxiliary classifier gan for improved covid-19 detection,” IEEE Access
2020
Later among the works it cites.
B. Ma, Y. Zhao, Y. Yang, X. Zhang, X. Dong, D. Zeng, S. Ma, and S. Li, “Mri image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation,” Computerized Medical Imaging and Graphics
2020
Later among the works it cites.
K. Tian, Y. Xu, J. Guan, and S. Zhou, “Network as regularization for training deep neural networks: Framework, model and performance,” Proceedings of the AAAI Conference on Artificial Intelligence
2020
Later among the works it cites.
W. Fang and X.-h. Han, “Spatial and channel attention modulated network for medical image segmentation,” in Proceedings of the Asian Conference on Computer Vision
2020
Later among the works it cites.
H. Zhao, J. Jia, and V. Koltun, “Exploring self-attention for image recognition,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2020
Later among the works it cites.
A. Sinha and J. Dolz, “Multi-scale self-guided attention for medical image segmentation,” IEEE journal of biomedical and health informatics
2020
Later among the works it cites.
Image/Video Understanding and Analysis (IUVA)
Q. Guan and Y. Huang, “Multi-label chest x-ray image classification via category-wise residual attention learning,” Pattern Recognition Letters · 2020
Later among the works it cites.
2020
Later among the works it cites.
G. López-García, J. M. Jerez, L. Franco, and F. J. Veredas, “Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data,” PloS one
2020
Later among the works it cites.
L. Alzubaidi, M. A. Fadhel, O. Al-Shamma, J. Zhang, J. Santamaría, Y. Duan, and S. R. Oleiwi, “Towards a better understanding of transfer learning for medical imaging: A case study,” Applied Sciences
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, and L. Farhan, “Review of deep learning: concepts, cnn architectures, challenges, applications, future directions,” Journal of Big Data
2021
Later among the works it cites.
S. He, K. T. Minn, L. Solnica-Krezel, M. A. Anastasio, and H. Li, “Deeply-supervised density regression for automatic cell counting in microscopy images,” Medical Image Analysis
2021
Later among the works it cites.
J. Geng, X. Zhang, S. Prabhu, S. H. Shahoei, E. R. Nelson, K. S. Swanson, M. A. Anastasio, and A. M. Smith, “3d microscopy and deep learning reveal the heterogeneity of crown-like structure microenvironments in intact adipose tissue,” Science Advances
2021
Later among the works it cites.
Z. Dai, H. Liu, Q. V. Le, and M. Tan, “Coatnet: Marrying convolution and attention for all data sizes,” Advances in Neural Information Processing Systems
2021
Later among the works it cites.
X. Zhang, E. C. Landsness, W. Chen, H. Miao, M. Tang, L. M. Brier, J. P. Culver, J.-M. Lee, and M. A. Anastasio, “Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning,” Journal of neuroscience methods
2022
Closest in time.
M. Saad, S. He, W. Thorstad, H. Gay, D. Barnett, Y. Zhao, S. Ruan, X. Wang, and H. Li, “Learning-based cancer treatment outcome prognosis using multimodal biomarkers,” IEEE Transactions on Radiation and Plasma Medical Sciences
2022
Closest in time.
K. Li, H. Li, and M. A. Anastasio, “A task-informed model training method for deep neural network-based image denoising,” in Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment
2022
Closest in time.
J. L. Granstedt, F. Li, U. Villa, and M. A. Anastasio, “Learned hotelling observers for use with multi-modal data,” in Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment
2022
Closest in time.