Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology

Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34

2014

Earlier work this paper cites.

Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34

2014

Earlier work this paper cites.

Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). pp. 565–571. Ieee (2016)

2016

Earlier work this paper cites.

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., Davatzikos, C.: Segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection (2017). DOI: https://doi. org/10.7937 K 9

2017

Earlier work this paper cites.

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4

2017

Earlier work this paper cites.

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4

2017

Earlier work this paper cites.

Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)

2017

Earlier work this paper cites.

Wang, S.: Challenges in medical image segmentation: A review. Medical Image Analysis 22

2017

Earlier work this paper cites.

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)

Original

2018

Earlier work this paper cites.

Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., Rozycki, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)

Original

2018

Earlier work this paper cites.

Jones, P.: Precision medicine in radiology: Enhancing patient care through image segmentation. Journal of Medical Imaging 27

2018

Cited alongside, same era.

Amgad, M., Elfandy, H., Hussein, H., Atteya, L.A., Elsebaie, M.A.T., Abo Elnasr, L.S., Sakr, R.A., Salem, H.S.E., Ismail, A.F., Saad, A.M., Ahmed, J., Elsebaie, M.A.T., Rahman, M., Ruhban, I.A., Elgazar, N.M., Alagha, Y., Osman, M.H., Alhusseiny, A.M., Khalaf, M.M., Younes, A.A.F., Abdulkarim, A., Younes, D.M., Gadallah, A.M., Elkashash, A.M., Fala, S.Y., Zaki, B.M., Beezley, J., Chittajallu, D.R., Manthey, D., Gutman, D.A., Cooper, L.A.D.: Structured crowdsourcing enables convolutional segmentation of histology images. Bioinformatics 35

2019

Cited alongside, same era.

Brown, A.: Advancements in medical image segmentation. Medical Imaging Journal 45

2019

Cited alongside, same era.

Hu, Y., Zhang, Y., Wei, Y., Huang, T.S.: Attention-based deep multiple instance learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 10465–10474 (2019)

Glatt, R., Liu, S.: Topological data analysis guided segment anything model prompt optimization for zero-shot segmentation in biological imaging. arXiv preprint arXiv:2306.17400 (2023)

Original

2023

Closest in time.

Gong, S., Zhong, Y., Ma, W., Li, J., Wang, Z., Zhang, J., Heng, P.A., Dou, Q.: 3dsam-adapter: Holistic adaptation of sam from 2d to 3d for promptable medical image segmentation. arXiv preprint arXiv:2306.13465 (2023)

Original

2023

Closest in time.

Hu, X., Xu, X., Shi, Y.: How to efficiently adapt large segmentation model (sam) to medical images. arXiv preprint arXiv:2306.13731 (2023)

Original

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.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)

Original

2023

Closest in time.