AutoPET Challenge: Combining nn-Unet with Swin UNETR Augmented by Maximum Intensity Projection Classifier

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

2016

Earlier work this paper cites.

Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019)

2019

Earlier work this paper cites.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

Original

2020

Earlier work this paper cites.

Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537 (2021)

Original

2021

Earlier work this paper cites.

Dai, Z., Liu, H., Le, Q.V., Tan, M.: Coatnet: Marrying convolution and attention for all data sizes. Advances in Neural Information Processing Systems 34

2021

Earlier work this paper cites.

Isensee, F., Jaeger, P., Kohl, S., et al.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18

2021

Cited alongside, same era.

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10012–10022 (2021)

2021

Cited alongside, same era.

Egger, J., Gsaxner, C., Pepe, A., Pomykala, K.L., Jonske, F., Kurz, M., Li, J., Kleesiek, J.: Medical deep learning–a systematic meta-review. Computer Methods and Programs in Biomedicine p. 106874 (2022)

2022

Cited alongside, same era.

Gatidis, S., Kuestner, T.: A whole-body fdg-pet/ct dataset with manually annotated tumor lesions (fdg-pet-ct-lesions)[dataset]. The Cancer Imaging Archive (2022). https://doi.org/DOI: 10.7937/gkr0-xv29

2022

Cited alongside, same era.

Georgi, T.W., Zieschank, A., Kornrumpf, K., Kurch, L., Sabri, O., Körholz, D., Mauz-Körholz, C., Kluge, R., Posch, S.: Automatic classification of lymphoma lesions in fdg-pet–differentiation between tumor and non-tumor uptake. PloS one 17

2022

Closest in time.

Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop. pp. 272–284. Springer (2022)

2022

Closest in time.

Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 574–584 (2022)

2022

Closest in time.

Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al.: Swin transformer v2: Scaling up capacity and resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12009–12019 (2022)