Fetching the paper…
Reading the bibliography…
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks.
2018 robotic scene segmentation challenge
Allan, M., Kondo, S., Bodenstedt, S., Leger, S., Kadkhodamohammadi, R., Luengo, I., Fuentes, F., Flouty, E., Mohammed, A., Pedersen, M., et al., 2020 · 2001
Earlier work this paper cites.
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
Earlier work this paper cites.
Nci-isbi 2013 challenge: automated segmentation of prostate structures
Bloch, N., Madabhushi, A., Huisman, H., Freymann, J., Kirby, J., Grauer, M., Enquobahrie, A., Jaffe, C., Clarke, L., Farahani, K., 2015 · 2013
Earlier work this paper cites.
Evaluation of prostate segmentation algorithms for mri: the promise12 challenge
Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al., 2014 · 2014
Earlier work this paper cites.
Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge, in: Proc. MICCAI Multi-Atlas Labeling Beyond Cranial Vault—Workshop Challenge, p. 12
Landman, B., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A., 2015 · 2015
Earlier work this paper cites.
Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review
Lemaître, G., Martí, R., Freixenet, J., Vilanova, J.C., Walker, P.M., Meriaudeau, F., 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Earlier work this paper cites.
Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European conference on computer vision (ECCV), pp. 801–818
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H., 2018 · 2018
Earlier work this paper cites.
Automatic instrument segmentation in robot-assisted surgery using deep learning, in: 2018 17th IEEE international conference on machine learning and applications (ICMLA), IEEE. pp. 624–628
Shvets, A.A., Rakhlin, A., Kalinin, A.A., Iglovikov, V.I., 2018 · 2018
Earlier work this paper cites.
Parameter-efficient transfer learning for nlp, in: International Conference on Machine Learning, PMLR. pp. 2790–2799
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S., 2019 · 2019
Earlier work this paper cites.
Ms-net: Multi-site network for improving prostate segmentation with heterogeneous mri data
Liu, Q., Dou, Q., Yu, L., Heng, P.A., 2020 · 2020
Earlier work this paper cites.
Task decomposition and synchronization for semantic biomedical image segmentation
Ren, X., Ahmad, S., Zhang, L., Xiang, L., Nie, D., Yang, F., Wang, Q., Shen, D., 2020 · 2020
Earlier work this paper cites.
On the opportunities and risks of foundation models
Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., et al., 2021 · 2021
Earlier work this paper cites.
Cross-attention is all you need: Adapting pretrained transformers for machine translation, in: Moens, M., Huang, X., Specia, L., Yih, S.W. (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, Association for Computational Linguistics. pp. 1754–1765
Gheini, M., Ren, X., May, J., 2021 · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., 2021 · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Scaling up visual and vision-language representation learning with noisy text supervision, in: International conference on machine learning, PMLR. pp. 4904–4916
Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T., 2021 · 2021
Earlier work this paper cites.
Test-time adaptable neural networks for robust medical image segmentation
Karani, N., Erdil, E., Chaitanya, K., Konukoglu, E., 2021 · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision, in: International conference on machine learning, PMLR. pp. 8748–8763
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al., 2021 · 2021
Cited alongside, same era.
Noisy-lstm: Improving temporal awareness for video semantic segmentation
Wang, B., Li, L., Nakashima, Y., Kawasaki, R., Nagahara, H., Yagi, Y., 2021 · 2021
Cited alongside, same era.
Florence: A new foundation model for computer vision
Yuan, L., Chen, D., Chen, Y.L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., et al., 2021 · 2021
Cited alongside, same era.
The medical segmentation decathlon
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., Ronneberger, O., et al., 2022 · 2022
3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable medical image segmentation
Gong, S., Zhong, Y., Ma, W., Li, J., Wang, Z., Zhang, J., Heng, P.A., Dou, Q., 2023 · 2023
Closest in time.
Accuracy of segment-anything model (sam) in medical image segmentation tasks
He, S., Bao, R., Li, J., Grant, P.E., Ou, Y., 2023 · 2023
Closest in time.
Hu, C., Li, X., 2023 · 2023
Closest in time.
How to efficiently adapt large segmentation model (sam) to medical images
Hu, X., Xu, X., Shi, Y., 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Towards a unified view of parameter-efficient transfer learning, in: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022, OpenReview.net
He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., Neubig, G., 2022 · 2022
Cited alongside, same era.
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Ji, Y., Bai, H., Yang, J., Ge, C., Zhu, Y., Zhang, R., Li, Z., Zhang, L., Ma, W., et al., 2022 · 2022
Cited alongside, same era.
Visual prompt tuning, in: European Conference on Computer Vision, Springer. pp. 709–727
Jia, M., Tang, L., Chen, B.C., Cardie, C., Belongie, S., Hariharan, B., Lim, S.N., 2022 · 2022
Cited alongside, same era.
Exploring intra-and inter-video relation for surgical semantic scene segmentation
Jin, Y., Yu, Y., Chen, C., Zhao, Z., Heng, P.A., Stoyanov, D., 2022 · 2022
Cited alongside, same era.
Single-domain generalization in medical image segmentation via test-time adaptation from shape dictionary, in: AAAI, pp. 1756–1764
Liu, Q., Chen, C., Dou, Q., Heng, P.A., 2022 · 2022
Cited alongside, same era.
St-adapter: Parameter-efficient image-to-video transfer learning
Pan, J., Lin, Z., Zhu, X., Shao, J., Li, H., 2022 · 2022
Cited alongside, same era.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models, in: Muresan, S., Nakov, P., Villavicencio, A. (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, Association for Computational Linguistics. pp. 1–9
Zaken, E.B., Goldberg, Y., Ravfogel, S., 2022 · 2022
Cited alongside, same era.
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al., 2023 · 2023
Closest in time.
Fact: Factor-tuning for lightweight adaptation on vision transformer, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1060–1068
Jie, S., Deng, Z.H., 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.
3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation, in: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023, OpenReview.net
Lee, H.H., Bao, S., Huo, Y., Landman, B.A., 2023 · 2023
Closest in time.
Auto-prompting sam for mobile friendly 3d medical image segmentation
Li, C., Khanduri, P., Qiang, Y., Sultan, R.I., Chetty, I., Zhu, D., 2023 · 2023
Closest in time.
Scaling down to scale up: A guide to parameter-efficient fine-tuning
Lialin, V., Deshpande, V., Rumshisky, A., 2023 · 2023
Closest in time.
Gpt understands, too
Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., Tang, J., 2023 · 2023
Closest in time.
Adaptivesam: Towards efficient tuning of sam for surgical scene segmentation
Paranjape, J.N., Nair, N.G., Sikder, S., Vedula, S.S., Patel, V.M., 2023 · 2023
Closest in time.
Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model, in: Medical Imaging with Deep Learning, short paper track
Wald, T., Roy, S., Koehler, G., Disch, N., Rokuss, M.R., Holzschuh, J., Zimmerer, D., Maier-Hein, K., 2023 · 2023
Closest in time.
Surgicalsam: Efficient class promptable surgical instrument segmentation
Yue, W., Zhang, J., Hu, K., Xia, Y., Luo, J., Wang, Z., 2023 · 2023
Closest in time.
Customized segment anything model for medical image segmentation
Zhang, K., Liu, D., 2023 · 2023
Closest in time.
Segment anything model (sam) for radiation oncology
Zhang, L., Liu, Z., Zhang, L., Wu, Z., Yu, X., Holmes, J., Feng, H., Dai, H., Li, X., Li, Q., et al., 2023 · 2023
Closest in time.
Segment everything everywhere all at once
Zou, X., Yang, J., Zhang, H., Li, F., Li, L., Gao, J., Lee, Y.J., 2023 · 2023
Closest in time.