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Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations.
Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation
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Making deep neural networks robust to label noise: A loss correction approach, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1944–1952
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L., 2017 · 1952
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Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules
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Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database
Van Ginneken, B., Stegmann, M.B., Loog, M., 2006 · 2006
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al., 2020 · 2010
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
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Kendall, A., Badrinarayanan, V., Cipolla, R., 2015 · 2015
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U-Net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Training convolutional networks with noisy labels, in: 3rd International Conference on Learning Representations, ICLR 2015
Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., Fergus, R., 2015 · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning, in: International Conference on Machine Learning, pp. 1050–1059
Gal, Y., Ghahramani, Z., 2016 · 2016
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Learning deep networks from noisy labels with dropout regularization, in: 2016 IEEE 16th International Conference on Data Mining (ICDM), IEEE. pp. 967–972
Jindal, I., Nokleby, M., Chen, X., 2016 · 2016
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Training deep neural-networks using a noise adaptation layer, in: ICLR
Goldberger, J., Ben-Reuven, E., 2017 · 2017
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Accurate lung segmentation via network-wise training of convolutional networks, in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, pp. 92–99
Hwang, S., Park, S., 2017 · 2017
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Co-teaching: Robust training of deep neural networks with extremely noisy labels, in: Advances in Neural Information Processing Systems, pp. 8527–8537
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., Sugiyama, M., 2018 · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise, in: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc
Hendrycks, D., Mazeika, M., Wilson, D., Gimpel, K., 2018 · 2018
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Joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning, in: Computational Pathology and Ophthalmic Medical Image Analysis. Springer, pp. 219–227
Sedai, S., Antony, B., Mahapatra, D., Garnavi, R., 2018 · 2018
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Non-local context encoder: Robust biomedical image segmentation against adversarial attacks, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 8417–8424
He, X., Yang, S., Li, G., Li, H., Chang, H., Yu, Y., 2019 · 2019
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Supervised uncertainty quantification for segmentation with multiple annotations, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 137–145
Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., Welling, M., 2019 · 2019
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Learning to segment skin lesions from noisy annotations, in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. Springer, pp. 207–215
Mirikharaji, Z., Yan, Y., Hamarneh, G., 2019 · 2019
Cited alongside, same era.
Dataset of breast ultrasound images
Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, Aly Fahmy, 2020 · 2019
Cited alongside, same era.
Zhu, H., Shi, J., Wu, J., 2019 · 2019
Cited alongside, same era.
Self-supervised learning for few-shot medical image segmentation
Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D., 2022 · 2022
Later among the works it cites.
Pseudo-label correction from pixel to image, in: 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC), IEEE. pp. 1–5
Wu, H., Wei, S., Tan, C., Zhao, Y., 2022 · 2022
Later among the works it cites.
Segment anything model (sam) enhanced pseudo labels for weakly supervised semantic segmentation
Chen, T., Mai, Z., Li, R., Chao, W.L., 2023 · 2023
Closest in time.
SAM on medical images: A comprehensive study on three prompt modes
Cheng, D., Qin, Z., Jiang, Z., Zhang, S., Lao, Q., Li, K., 2023 · 2023
Closest in time.
All-in-SAM: from weak annotation to pixel-wise nuclei segmentation with prompt-based finetuning
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Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A., 2020 · 2020
Cited alongside, same era.
Semi-supervised semantic image segmentation with self-correcting networks, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12715–12725
Ibrahim, M.S., Vahdat, A., Ranjbar, M., Macready, W.G., 2020 · 2020
Cited alongside, same era.
Lung segmentation dataset
Konya, 2020 · 2020
Cited alongside, same era.
An effective data refinement approach for upper gastrointestinal anatomy recognition, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 43–52
Quan, L., Li, Y., Chen, X., Zhang, N., 2020 · 2020
Cited alongside, same era.
FCN based label correction for multi-atlas guided organ segmentation
Zhu, H., Adeli, E., Shi, F., Shen, D., Initiative, A.D.N., 2020 · 2020
Cited alongside, same era.
Co-seg: An image segmentation framework against label corruption
Huang, Z., Zhang, H., Laine, A., Angelini, E., Hendon, C., Gan, Y., 2021 · 2021
Cited alongside, same era.
Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams
Shen, Y., Shamout, F.E., Oliver, J.R., Witowski, J., Kannan, K., Park, J., Wu, N., Huddleston, C., Wolfson, S., Millet, A., Ehrenpreis, R., Awal, D., Tyma, C., Samreen, N., Gao, Y., Chhor, C., Gandhi, S., Lee, C., Kumari-Subaiya, S., Leonard, C., Mohammed, R., Moczulski, C., Altabet, J., Babb, J., Lewin, A., Reig, B., Moy, L., Heacock, L., Geras, K.J., 2021 · 2021
Cited alongside, same era.
Distilling effective supervision for robust medical image segmentation with noisy labels, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24, Springer. pp. 668–677
Shi, J., Wu, J., 2021 · 2021
Cited alongside, same era.
Cui, C., Deng, R., Liu, Q., Yao, T., Bao, S., Remedios, L.W., Tang, Y., Huo, Y., 2023 · 2023
Closest in time.
Deng, R., Cui, C., Liu, Q., Yao, T., Remedios, L.W., Bao, S., Landman, B.A., Wheless, L.E., Coburn, L.A., Wilson, K.T., et al., 2023 · 2023
Closest in time.
Weakly-supervised semantic segmentation via online pseudo-mask correcting
Feng, J., Wang, X., Li, T., Ji, S., Liu, W., 2023 · 2023
Closest in time.
Hu, C., Li, X., 2023 · 2023
Closest in time.
Segment anything is a good pseudo-label generator for weakly supervised semantic segmentation
Jiang, P.T., Yang, Y., 2023 · 2023
Closest in time.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al., 2023 · 2023
Closest in time.
Segment anything model for semi-supervised medical image segmentation via selecting reliable pseudo-labels
Li, N., Xiong, L., Qiu, W., Pan, Y., Luo, Y., Zhang, Y., 2023 · 2023
Closest in time.
Av-sam: Segment anything model meets audio-visual localization and segmentation
Mo, S., Tian, Y., 2023 · 2023
Closest in time.
SAM.MD: Zero-shot medical image segmentation capabilities of the segment anything model
Roy, S., Wald, T., Koehler, G., Rokuss, M.R., Disch, N., Holzschuh, J., Zimmerer, D., Maier-Hein, K.H., 2023 · 2023
Closest in time.
Sun, W., Liu, Z., Zhang, Y., Zhong, Y., Barnes, N., 2023 · 2023
Closest in time.
Scaling-up remote sensing segmentation dataset with segment anything model
Wang, D., Zhang, J., Du, B., Tao, D., Zhang, L., 2023 · 2023
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How segment anything model (SAM) boost medical image segmentation?
Zhang, Y., Jiao, R., 2023 · 2023
Closest in time.