Fetching the paper…
Reading the bibliography…
Deep Learning (DL) holds great promise in reshaping the industry owing to its precision, efficiency, and objectivity.
G. E. Hinton and D. Van Camp, “Keeping the neural networks simple by minimizing the description length of the weights,” in Proceedings of the sixth annual conference on Computational learning theory , 1993, pp. 5–13
1993
Earlier work this paper cites.
G. P. Mazzara, R. P. Velthuizen, J. L. Pearlman, H. M. Greenberg, and H. Wagner, “Brain tumor target volume determination for radiation treatment planning through automated mri segmentation,” International Journal of Radiation Oncology* Biology* Physics , vol. 59, no. 1, pp. 300–312, 2004
2004
Earlier work this paper cites.
A. Graves, “Practical Variational Inference for Neural Networks,” in Advances in neural information processing systems , 2011, pp. 2348–2356
2011
Earlier work this paper cites.
B. E. Nelms, W. A. Tomé, G. Robinson, and J. Wheeler, “Variations in the contouring of organs at risk: test case from a patient with oropharyngeal cancer,” International Journal of Radiation Oncology* Biology* Physics , vol. 82, no. 1, pp. 368–378, 2012
2012
Earlier work this paper cites.
C. F. Njeh, L. Dong, and C. G. Orton, “Igrt has limited clinical value due to lack of accurate tumor delineation,” Medical Physics , vol. 40, no. 4, p. 040601, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest et al. , “The multimodal brain tumor image segmentation benchmark (brats),” IEEE transactions on medical imaging , vol. 34, no. 10, pp. 1993–2024, 2014
2014
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” in international conference on machine learning , 2016, pp. 1050–1059
2016
Earlier work this paper cites.
M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, “The importance of skip connections in biomedical image segmentation,” in Deep learning and data labeling for medical applications . Springer, 2016, pp. 179–187
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and 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.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. K. Vinod, M. Min, M. G. Jameson, and L. C. Holloway, “A review of interventions to reduce inter-observer variability in volume delineation in radiation oncology,” Journal of medical imaging and radiation oncology , vol. 60, no. 3, pp. 393–406, 2016
2016
Earlier work this paper cites.
C. W. Wee, W. Sung, H.-C. Kang, K. H. Cho, T. J. Han, B.-K. Jeong, J.-U. Jeong, H. Kim, I. A. Kim, J. H. Kim et al. , “Evaluation of variability in target volume delineation for newly diagnosed glioblastoma: a multi-institutional study from the korean radiation oncology group,” Radiation Oncology , vol. 10, no. 1, pp. 1–9, 2016
2016
Earlier work this paper cites.
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” nature , vol. 542, no. 7639, pp. 115–118, 2017
2017
Earlier work this paper cites.
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in International Conference on Machine Learning . PMLR, 2017, pp. 1321–1330
2017
Earlier work this paper cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles,” in Advances in neural information processing systems , 2017, pp. 6402–6413
2017
Earlier work this paper cites.
X. Lu and B. Van Roy, “Ensemble Sampling,” in Advances in neural information processing systems , 2017, pp. 3258–3266
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 4, pp. 834–848, 2017
2017
Earlier work this paper cites.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 12, pp. 2481–2495, 2017
2017
Earlier work this paper cites.
S. Jégou, M. Drozdzal, D. Vazquez, A. Romero, and Y. Bengio, “The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2017, pp. 11–19
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4700–4708
2017
Cited alongside, same era.
A. Jungo, R. McKinley, R. Meier, U. Knecht, L. Vera, J. Pérez-Beteta, D. Molina-García, V. M. Pérez-García, R. Wiest, and M. Reyes, “Towards Uncertainty-Assisted Brain Tumor Segmentation and Survival Prediction,” in International MICCAI Brainlesion Workshop . Springer, 2017, pp. 474–485
2017
Cited alongside, same era.
K. Kamnitsas, W. Bai, E. Ferrante, S. McDonagh, M. Sinclair, N. Pawlowski, M. Rajchl, M. Lee, B. Kainz, D. Rueckert et al. , “Ensembles of multiple models and architectures for robust brain tumour segmentation,” in International MICCAI brainlesion workshop . Springer, 2017, pp. 450–462
M. U. Rehman, S. Cho, J. H. Kim, and K. T. Chong, “Bu-net: Brain tumor segmentation using modified u-net architecture,” Electronics , vol. 9, no. 12, p. 2203, 2020
2020
Later among the works it cites.
T. Nair, D. Precup, D. L. Arnold, and T. Arbel, “Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation,” Medical image analysis , vol. 59, p. 101557, 2020
2020
Later among the works it cites.
Y. Kwon, J.-H. Won, B. J. Kim, and M. C. Paik, “Uncertainty quantification using bayesian neural networks in classification: Application to biomedical image segmentation,” Computational Statistics & Data Analysis , vol. 142, p. 106816, 2020
2020
Later among the works it cites.
X. Feng, N. J. Tustison, S. H. Patel, and C. H. Meyer, “Brain tumor segmentation using an ensemble of 3d u-nets and overall survival prediction using radiomic features,” Frontiers in computational neuroscience , vol. 14, p. 25, 2020
2020
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.
2017
Cited alongside, same era.
M. L. Giger, “Machine learning in medical imaging,” Journal of the American College of Radiology , vol. 15, no. 3, pp. 512–520, 2018
2018
Cited alongside, same era.
D. S. Ting, Y. Liu, P. Burlina, X. Xu, N. M. Bressler, and T. Y. Wong, “Ai for medical imaging goes deep,” Nature medicine , vol. 24, no. 5, pp. 539–540, 2018
2018
Cited alongside, same era.
B. Biggio and F. Roli, “Wild patterns: Ten years after the rise of adversarial machine learning,” Pattern Recognition , vol. 84, pp. 317–331, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
H. Sandström, H. Jokura, C. Chung, and I. Toma-Dasu, “Multi-institutional study of the variability in target delineation for six targets commonly treated with radiosurgery,” Acta Oncologica , vol. 57, no. 11, pp. 1515–1520, 2018
2018
Cited alongside, same era.
M. Pavic, M. Bogowicz, X. Würms, S. Glatz, T. Finazzi, O. Riesterer, J. Roesch, L. Rudofsky, M. Friess, P. Veit-Haibach et al. , “Influence of inter-observer delineation variability on radiomics stability in different tumor sites,” Acta Oncologica , vol. 57, no. 8, pp. 1070–1074, 2018
2018
Cited alongside, same era.
X. Liu, L. Faes, A. U. Kale, S. K. Wagner, D. J. Fu, A. Bruynseels, T. Mahendiran, G. Moraes, M. Shamdas, C. Kern et al. , “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis,” The lancet digital health , vol. 1, no. 6, pp. e271–e297, 2019
2019
Cited alongside, same era.
M. G. B. Calisto and S. K. Lai-Yuen, “Self-adaptive 2d-3d ensemble of fully convolutional networks for medical image segmentation,” in Medical Imaging 2020: Image Processing , vol. 11313. International Society for Optics and Photonics, 2020, p. 113131W
2020
Later among the works it cites.
K. Hoebel, V. Andrearczyk, A. Beers, J. Patel, K. Chang, A. Depeursinge, H. Müller, and J. Kalpathy-Cramer, “An exploration of uncertainty information for segmentation quality assessment,” in Medical Imaging 2020: Image Processing , vol. 11313. International Society for Optics and Photonics, 2020, p. 113131K
2020
Later among the works it cites.
M. Jun, G. Cheng, W. Yixin, A. Xingle, G. Jiantao, Y. Ziqi, Z. Minqing, L. Xin, D. Xueyuan, C. Shucheng, W. Hao, M. Sen, Y. Xiaoyu, N. Ziwei, L. Chen, T. Lu, Z. Yuntao, Z. Qiongjie, D. Guoqiang, and H. Jian, “COVID-19 CT Lung and Infection Segmentation Dataset,” Apr. 2020. [Online]. Available: https://doi.org/10.5281/zenodo.3757476
2020
Later among the works it cites.
S. Jadon, “A survey of loss functions for semantic segmentation,” in 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) . IEEE, 2020, pp. 1–7
2020
Later among the works it cites.
A. Mehrtash, W. M. Wells, C. M. Tempany, P. Abolmaesumi, and T. Kapur, “Confidence calibration and predictive uncertainty estimation for deep medical image segmentation,” IEEE transactions on medical imaging , vol. 39, no. 12, pp. 3868–3878, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
H. Hirano, A. Minagi, and K. Takemoto, “Universal adversarial attacks on deep neural networks for medical image classification,” BMC medical imaging , vol. 21, no. 1, pp. 1–13, 2021
2021
Closest in time.
D. Dera, N. C. Bouaynaya, G. Rasool, R. Shterenberg, and H. M. Fathallah-Shaykh, “Premium-cnn: Propagating uncertainty towards robust convolutional neural networks,” IEEE Transactions on Signal Processing , vol. 69, pp. 4669–4684, 2021
2021
Closest in time.
G. Carannante, N. C. Bouaynaya, and L. Mihaylova, “An enhanced particle filter for uncertainty quantification in neural networks,” in 2021 IEEE 24th International Conference on Information Fusion (FUSION) . IEEE, 2021, pp. 1–7
2021
Closest in time.
S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Closest in time.
S.-T. Tran, C.-H. Cheng, T.-T. Nguyen, M.-H. Le, and D.-G. Liu, “Tmd-unet: Triple-unet with multi-scale input features and dense skip connection for medical image segmentation,” in Healthcare , vol. 9, no. 1. Multidisciplinary Digital Publishing Institute, 2021, p. 54
2021
Closest in time.
X. Li, W. Qian, D. Xu, and C. Liu, “Image segmentation based on improved unet,” in Journal of Physics: Conference Series , vol. 1815, no. 1. IOP Publishing, 2021, p. 012018
2021
Closest in time.
A. J. Larrazabal, C. Martínez, J. Dolz, and E. Ferrante, “Orthogonal ensemble networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2021, pp. 594–603
2021
Closest in time.
B. Ghoshal, A. Tucker, B. Sanghera, and W. Lup Wong, “Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection,” Computational Intelligence , vol. 37, no. 2, pp. 701–734, 2021
2021
Closest in time.
B. McCrindle, K. Zukotynski, T. E. Doyle, and M. D. Noseworthy, “A radiology-focused review of predictive uncertainty for ai interpretability in computer-assisted segmentation,” Radiology: Artificial Intelligence , vol. 3, no. 6, 2021
2021
Closest in time.
K. Kim, M. Chun, H. Jin, W. Jung, K. H. Shin, S. S. Shin, Y. J. Kim, S.-H. Park, J. H. Kim, Y. H. Kim et al. , “Inter-institutional variation in intensity-modulated radiotherapy for breast cancer in korea (krog 19-01),” Anticancer Research , vol. 41, no. 6, pp. 3145–3152, 2021
2021
Closest in time.
2021
Closest in time.
B. Lambert, F. Forbes, S. Doyle, A. Tucholka, and M. Dojat, “Fast uncertainty quantification for deep learning-based mr brain segmentation,” in Conference francophone pour l’extraction et la gestion des connaissances , 2022
2022
Closest in time.
H. Asgharnezhad, A. Shamsi, R. Alizadehsani, A. Khosravi, S. Nahavandi, Z. A. Sani, D. Srinivasan, and S. M. S. Islam, “Objective evaluation of deep uncertainty predictions for covid-19 detection,” Scientific Reports , vol. 12, no. 1, pp. 1–11, 2022
2022
Closest in time.