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Whole abdominal organ segmentation is important in diagnosing abdomen lesions, radiotherapy, and follow-up.
The liver tumor segmentation benchmark (lits)
Bilic, P., Christ, P.F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., Fu, C.W., Han, X., Heng, P.A., Hesser, J., et al., 2019 · 1901
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Algorithms for clustering data
Jain, A.K., Dubes, R.C., 1988 · 1988
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Active contours without edges
Chan, T.F., Vese, L.A., 2001 · 2001
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Semi-supervised learning by entropy minimization
Grandvalet, Y., Bengio, Y., et al., 2005 · 2005
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User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability
Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G., 2006 · 2006
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J., et al., 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation, in: MICCAI, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation, in: MICCAI, Springer. pp. 556–564
Roth, H.R., Lu, L., Farag, A., Shin, H.C., Liu, J., Turkbey, E.B., Summers, R.M., 2015 · 2015
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Scribblesup: Scribble-supervised convolutional networks for semantic segmentation, in: CVPR, pp. 3159–3167
Lin, D., Dai, J., Jia, J., He, K., Sun, J., 2016 · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L., 2017 · 2017
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Multi-atlas labeling beyond the cranial vault-workshop and challenge
Landman, B., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A., 2017 · 2017
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Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy
Mishra, A., Marr, D., 2017 · 2017
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Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss, in: IJCAI
Dou, Q., Ouyang, C., Chen, C., Chen, H., Heng, P.A., 2018 · 2018
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Automatic multi-organ segmentation on abdominal ct with dense v-networks
Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S.P., Clarkson, M.J., Barratt, D.C., 2018 · 2018
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3d-espnet with pyramidal refinement for volumetric brain tumor image segmentation, in: International MICCAI Brainlesion Workshop, Springer. pp. 245–253
Nuechterlein, N., Mehta, S., 2018 · 2018
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Attention u-net: Learning where to look for the pancreas
Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al., 2018 · 2018
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A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography
Oliveira, B., Queirós, S., Morais, P., Torres, H.R., Gomes-Fonseca, J., Fonseca, J.C., Vilaça, J.L., 2018 · 2018
Cited alongside, same era.
DeepIGeoS: a deep interactive geodesic framework for medical image segmentation
Wang, G., Zuluaga, M.A., Li, W., Pratt, R., Patel, P.A., Aertsen, M., Doel, T., David, A.L., Deprest, J., Ourselin, S., et al., 2018 · 2018
Cited alongside, same era.
Shufflenet: An extremely efficient convolutional neural network for mobile devices, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6848–6856
Zhang, X., Zhou, X., Lin, M., Sun, J., 2018 · 2018
Cited alongside, same era.
3d dilated multi-fiber network for real-time brain tumor segmentation in mri, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 184–192
Chen, C., Liu, X., Ding, M., Zheng, J., Li, J., 2019 · 2019
Cited alongside, same era.
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Xia, Y., Yang, D., Yu, Z., Liu, F., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A., Roth, H., 2020 · 2020
Later among the works it cites.
Unsupervised wasserstein distance guided domain adaptation for 3d multi-domain liver segmentation, in: Interpretable and Annotation-Efficient Learning for Medical Image Computing. Springer, pp. 155–163
You, C., Yang, J., Chapiro, J., Duncan, J.S., 2020 · 2020
Later among the works it cites.
Weakly-supervised salient object detection via scribble annotations, in: CVPR, pp. 12546–12555
Zhang, J., Yu, X., Li, A., Song, P., Liu, B., Dai, Y., 2020 · 2020
Later among the works it cites.
Swin-unet: Unet-like pure transformer for medical image segmentation
Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M., 2021 · 2021
Closest in time.
Resolution-aware knowledge distillation for efficient inference
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Pytorch: An imperative style, high-performance deep learning library, in: NeurIPS, pp. 8026–8037
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al., 2019 · 2019
Cited alongside, same era.
Attention gated networks: Learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., Rueckert, D., 2019 · 2019
Cited alongside, same era.
Clinically applicable deep learning framework for organs at risk delineation in ct images
Tang, H., Chen, X., Liu, Y., Lu, Z., You, J., Yang, M., Yao, S., Zhao, G., Xu, Y., Chen, T., et al., 2019 · 2019
Cited alongside, same era.
Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation, in: CVPR, pp. 2517–2526
Vu, T.H., Jain, H., Bucher, M., Cord, M., Pérez, P., 2019 · 2019
Cited alongside, same era.
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion
Wang, Y., Zhou, Y., Shen, W., Park, S., Fishman, E.K., Yuille, A.L., 2019 · 2019
Cited alongside, same era.
Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data
Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C., 2020 · 2020
Cited alongside, same era.
Unpaired multi-modal segmentation via knowledge distillation
Dou, Q., Liu, Q., Heng, P.A., Glocker, B., 2020 · 2020
Cited alongside, same era.
Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search, in: CVPR, pp. 4223–4232
Guo, D., Jin, D., Zhu, Z., Ho, T.Y., Harrison, A.P., Chao, C.H., Xiao, J., Lu, L., 2020 · 2020
Cited alongside, same era.
Feng, Z., Lai, J., Xie, X., 2021 · 2021
Closest in time.
nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H., 2021 · 2021
Closest in time.
Incorporating the hybrid deformable model for improving the performance of abdominal ct segmentation via multi-scale feature fusion network
Liang, X., Li, N., Zhang, Z., Xiong, J., Zhou, S., Xie, Y., 2021 · 2021
Closest in time.
Abdomenct-1k: Is abdominal organ segmentation a solved problem
Ma, J., Zhang, Y., Gu, S., Zhu, C., Ge, C., Zhang, Y., An, X., Wang, C., Wang, Q., Liu, X., et al., 2021 · 2021
Closest in time.
Efficient medical image segmentation based on knowledge distillation
Qin, D., Bu, J.J., Liu, Z., Shen, X., Zhou, S., Gu, J.J., Wang, Z.H., Wu, L., Dai, H.F., 2021 · 2021
Closest in time.
High-resolution 3d abdominal segmentation with random patch network fusion
Tang, Y., Gao, R., Lee, H.H., Han, S., Chen, Y., Gao, D., Nath, V., Bermudez, C., Savona, M.R., Abramson, R.G., et al., 2021 · 2021
Closest in time.
Learning to segment from scribbles using multi-scale adversarial attention gates
Valvano, G., Leo, A., Tsaftaris, S.A., 2021 · 2021
Closest in time.
CoTr: Efficiently bridging cnn and transformer for 3d medical image segmentation, in: MICCAI, Springer. pp. 171–180
Xie, Y., Zhang, J., Shen, C., Xia, Y., 2021 · 2021
Closest in time.
You, C., Zhao, R., Staib, L., Duncan, J.S., 2021 · 2021
Closest in time.
Structure-consistent weakly supervised salient object detection with local saliency coherence, in: AAAI
Yu, S., Zhang, B., Xiao, J., Lim, E.G., 2021 · 2021
Closest in time.
Lcov-net: A lightweight neural network for covid-19 pneumonia lesion segmentation from 3d ct images, in: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE. pp. 42–45
Zhao, Q., Wang, H., Wang, G., 2021 · 2021
Closest in time.
Unetr: Transformers for 3d medical image segmentation, in: WACV, pp. 574–584
Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D., 2022 · 2022
Closest in time.