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Uncertainty estimation is important for interpreting the trustworthiness of machine learning models in many applications.
Active Learning with Statistical Models
Cohn, D. A., Ghahramani, Z., and Jordan, M. I · 1996
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Evaluating predictive uncertainty challenge
Candela, J. Q., Rasmussen, C. E., Sinz, F. H., Bousquet, O., and Schölkopf, B · 2005
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Bayesian active learning for classification and preference learning
Houlsby, N., Huszar, F., Ghahramani, Z., and Lengyel, M · 2011
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei
Song, Y., Zhang, L., Chen, S., Ni, D., Li, B., Zhou, Y., Lei, B., and Wang, T · 2014
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Deeply-supervised nets
Lee, C., Xie, S., Gallagher, P. W., Zhang, Z., and Tu, Z · 2015
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Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning
Song, Y., Zhang, L., Chen, S., Ni, D., Lei, B., and Wang, T · 2015
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Training deeper convolutional networks with deep supervision
Wang, L., Lee, C., Tu, Z., and Lazebnik, S · 2015
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Holistically-nested edge detection
Xie, S. and Tu, Z · 2015
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Multi-loss convolutional networks for gland analysis in microscopy
BenTaieb, A., Kawahara, J., and Hamarneh, G · 2016
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DCAN: deep contour-aware networks for accurate gland segmentation
Chen, H., Qi, X., Yu, L., and Heng, P · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Gland segmentation in colon histology images: The glas challenge contest
Sirinukunwattana, K., Pluim, J. P. W., Chen, H., Qi, X., Heng, P., Guo, Y. B., Wang, L. Y., Matuszewski, B. J., Bruni, E., Sanchez, U., Böhm, A., Ronneberger, O., Cheikh, B. B., Racoceanu, D., Kainz, P., Pfeiffer, M., Urschler, M., Snead, D. R. J., and Rajpoot, N. M · 2016
Cited alongside, same era.
Cutting out the middleman: Measuring nuclear area in histopathology slides without segmentation
Veta, M., van Diest, P. J., and Pluim, J. P. W · 2016
Cited alongside, same era.
The power of ensembles for active learning in image classification
Beluch, W. H., Genewein, T., Nürnberger, A., and Köhler, J. M · 2018
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Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3d ct data
Chmelik, J., Jakubicek, R., Walek, P., Jan, J., Ourednicek, P., Lambert, L., Amadori, E., and Gavelli, G · 2018
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (ISIC)
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N. K., Kittler, H., and Halpern, A · 2018
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Active multitask learning for object recognition in images using deep neural networks, 2018
Li, B · 2018
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CEREALS - cost-effective region-based active learning for semantic segmentation
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Xu, Y., Li, Y., Liu, M., Wang, Y., Lai, M., and Chang, E. I · 2016
Cited alongside, same era.
Deep bayesian active learning with image data
Gal, Y., Islam, R., and Ghahramani, Z · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Empirical evaluation of the cycle reservoir with regular jumps for time series forecasting: A comparison study
Qasem, M. H., Faris, H., Rodan, A., and Sheta, A. F · 2017
Cited alongside, same era.
Gland instance segmentation using deep multichannel neural networks
Xu, Y., Li, Y., Wang, Y., Liu, M., Fan, Y., Lai, M., and Chang, E. I · 2017
Cited alongside, same era.
Suggestive annotation: A deep active learning framework for biomedical image segmentation
Yang, L., Zhang, Y., Chen, J., Zhang, S., and Chen, D. Z · 2017
Cited alongside, same era.
Mackowiak, R., Lenz, P., Ghori, O., Diego, F., Lange, O., and Rother, C · 2018
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Viewal: Active learning with viewpoint entropy for semantic segmentation
Siddiqui, Y., Valentin, J., and Nießner, M · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J. V., Ren, J., and Nado, Z · 2019
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Thulasidasan, S., Chennupati, G., Bilmes, J. A., Bhattacharya, T., and Michalak, S · 2019
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Reinforced active learning for image segmentation
Casanova, A., Pinheiro, P. O., Rostamzadeh, N., and Pal, C. J · 2020
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Dtu data
DTU · 2020
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