Fetching the paper…
Reading the bibliography…
The unprecedented developments in segmentation foundational models have become a dominant force in the field of computer vision, introducing a multitude of previously unexplored capabilities in a wide range of natural images and videos.
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Medical image analysis 42
2017
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. In: International Conference on Learning Representations (2020)
2020
Earlier work this paper cites.
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods 18
2021
Earlier work this paper cites.
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B.A., Litjens, G., Menze, B., Ronneberger, O., Summers, R.M., et al.: The medical segmentation decathlon. Nature communications 13
2022
Earlier work this paper cites.
Ma, J., Zhang, Y., Gu, S., Zhu, C., Ge, C., Zhang, Y., An, X., Wang, C., Wang, Q., Liu, X., Cao, S., Zhang, Q., Liu, S., Wang, Y., Li, Y., He, J., Yang, X.: Abdomenct-1k: Is abdominal organ segmentation a solved problem? IEEE Transactions on Pattern Analysis and Machine Intelligence 44
2022
Earlier work this paper cites.
Willemink, M.J., Roth, H.R., Sandfort, V.: Toward foundational deep learning models for medical imaging in the new era of transformer networks. Radiology. Artificial intelligence 46
2022
Earlier work this paper cites.
Zhang, Y., Liao, Q., Ding, L., Zhang, J.: Bridging 2d and 3d segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5 d solutions. Computerized Medical Imaging and Graphics p. 102088 (2022)
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
Moor, M., Banerjee, O., Abad, Z.F.H., Krumholz, H.M., Leskovec, J., Topol, E.J., Rajpurkar, P.: Foundation models for generalist medical artificial intelligence. Nature 616
2023
Earlier work this paper cites.
Wang, X., Chen, G., Qian, G., Gao, P., Wei, X.Y., Wang, Y., Tian, Y., Gao, W.: Large-scale multi-modal pre-trained models: A comprehensive survey. Machine Intelligence Research pp. 1–36 (2023)
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
Chen, R.J., Ding, T., Lu, M.Y., Williamson, D.F., Jaume, G., Song, A.H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al.: Towards a general-purpose foundation model for computational pathology. Nature Medicine 30
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2024
Cited alongside, same era.
2024
Cited alongside, same era.
Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nature Communications 15
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Zhang, Y., Shen, Z., Jiao, R.: Segment anything model for medical image segmentation: Current applications and future directions. Computers in Biology and Medicine 171
2024
Closest in time.