Fetching the paper…
Reading the bibliography…
Medical image and video segmentation is a critical task for precision medicine, which has witnessed considerable progress in developing task or modality-specific and generalist models for 2D images.
E. A. Eisenhauer, P. Therasse, J. Bogaerts, L. H. Schwartz, D. Sargent, R. Ford, J. Dancey, S. Arbuck, S. Gwyther, M. Mooney et al. , “New response evaluation criteria in solid tumours: revised recist guideline (version 1.1),” European Journal of Cancer , vol. 45, no. 2, pp. 228–247, 2009
2009
Earlier work this paper cites.
R. Kikinis, S. D. Pieper, and K. G. Vosburgh, “3d slicer: a platform for subject-specific image analysis, visualization, and clinical support,” in Intraoperative imaging and image-guided therapy . Springer, 2013, pp. 277–289
2013
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention , 2015, pp. 234–241
2015
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 2117–2125
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 40, no. 4, pp. 834–848, 2018
2018
Earlier work this paper cites.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European Conference on Computer Vision , 2018, pp. 801–818
2018
Earlier work this paper cites.
T. Falk, D. Mai, R. Bensch, O. undefinediçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: deep learning for cell counting, detection, and morphometry,” Nature Methods , vol. 16, no. 1, p. 67–70, 2018
2018
Earlier work this paper cites.
K. Yan, X. Wang, L. Lu, and R. M. Summers, “Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning,” Journal of Medical Imaging , vol. 5, no. 3, pp. 036 501–036 501, 2018
2018
Earlier work this paper cites.
S. Leclerc, E. Smistad, J. Pedrosa, A. Østvik, F. Cervenansky, F. Espinosa, T. Espeland, E. A. R. Berg, P.-M. Jodoin, T. Grenier et al. , “Deep learning for segmentation using an open large-scale dataset in 2d echocardiography,” IEEE Transactions on Medical Imaging , vol. 38, no. 9, pp. 2198–2210, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations , 2019
2019
Earlier work this paper cites.
D. Ouyang, B. He, A. Ghorbani, N. Yuan, J. Ebinger, C. P. Langlotz, P. A. Heidenreich, R. A. Harrington, D. H. Liang, E. A. Ashley, and J. Y. Zou, “Video-based ai for beat-to-beat assessment of cardiac function,” Nature , vol. 580, no. 7802, pp. 252–256, 2020
2020
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly 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.
F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,” Nature Methods , vol. 18, no. 2, pp. 203–211, 2021
2021
Earlier work this paper cites.
M. Misawa, S.-e. Kudo, Y. Mori, K. Hotta, K. Ohtsuka, T. Matsuda, S. Saito, T. Kudo, T. Baba, F. Ishida, H. Itoh, M. Oda, and K. Mori, “Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video),” Gastrointestinal Endoscopy , vol. 93, no. 4, pp. 960–967.e3, 2021
2021
Earlier work this paper cites.
G.-P. Ji, G. Xiao, Y.-C. Chou, D.-P. Fan, K. Zhao, G. Chen, and L. Van Gool, “Video polyp segmentation: A deep learning perspective,” Machine Intelligence Research , vol. 19, no. 6, pp. 531–549, 2022
2022
Earlier work this paper cites.
K. Cao, Y. Xia, J. Yao, X. Han, L. Lambert, T. Zhang, W. Tang, G. Jin, H. Jiang, X. Fang et al. , “Large-scale pancreatic cancer detection via non-contrast ct and deep learning,” Nature medicine , vol. 29, no. 12, pp. 3033–3043, 2023
2023
Earlier work this paper cites.
B. He, A. C. Kwan, J. H. Cho, N. Yuan, C. Pollick, T. Shiota, J. Ebinger, N. A. Bello, J. Wei, K. Josan, G. Duffy, M. Jujjavarapu, R. Siegel, S. Cheng, J. Y. Zou, and D. Ouyang, “Blinded, randomized trial of sonographer versus AI cardiac function assessment,” Nature , vol. 616, no. 7957, pp. 520–524, 2023
2023
Earlier work this paper cites.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollar, and R. Girshick, “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
Cited alongside, same era.
M. Moor, O. Banerjee, Z. S. H. Abad, H. M. Krumholz, J. Leskovec, E. J. Topol, and P. Rajpurkar, “Foundation models for generalist medical artificial intelligence,” Nature , vol. 616, no. 7956, pp. 259–265, 2023
2023
Cited alongside, same era.
M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, “Segment anything model for medical image analysis: an experimental study,” Medical Image Analysis , vol. 89, p. 102918, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Du, F. Bai, T. Huang, and B. Zhao, “Segvol: Universal and interactive volumetric medical image segmentation,” in Advances in Neural Information Processing Systems , vol. 37, 2024, pp. 110 746–110 783
2024
Later among the works it cites.
Y. He, P. Guo, Y. Tang, A. Myronenko, V. Nath, Z. Xu, D. Yang, C. Zhao, B. Simon, M. Belue, S. Harmon, B. Turkbey, D. Xu, and W. Li, “VISTA3D: A unified segmentation foundation model for 3D medical imaging,” in Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Ryali, Y.-T. Hu, D. Bolya, C. Wei, H. Fan, P.-Y. Huang, V. Aggarwal, A. Chowdhury, O. Poursaeed, J. Hoffman et al. , “Hiera: A hierarchical vision transformer without the bells-and-whistles,” in International conference on machine learning . PMLR, 2023, pp. 29 441–29 454
2023
Cited alongside, same era.
L. Ke, M. Ye, M. Danelljan, Y. liu, Y.-W. Tai, C.-K. Tang, and F. Yu, “Segment anything in high quality,” in Advances in Neural Information Processing Systems , vol. 36, 2023, pp. 29 914–29 934
2023
Cited alongside, same era.
B. Magyar, M. Tokodi, A. Soós, M. Tolvaj, B. K. Lakatos, A. Fábián, E. Surkova, B. Merkely, A. Kovács, and A. Horváth, “Rvenet: A large echocardiographic dataset for the deep learning-based assessment of right ventricular function,” in Computer Vision – ECCV 2022 Workshops . Springer Nature Switzerland, 2023, p. 569–583
2023
Cited alongside, same era.
M. Tokodi, B. Magyar, A. Soós, M. Takeuchi, M. Tolvaj, B. K. Lakatos, T. Kitano, Y. Nabeshima, A. Fábián, M. B. Szigeti et al. , “Deep learning-based prediction of right ventricular ejection fraction using 2d echocardiograms,” Cardiovascular Imaging , vol. 16, no. 8, pp. 1005–1018, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y.-R. Wang, K. Yang, Y. Wen, P. Wang, Y. Hu, Y. Lai, Y. Wang, K. Zhao, S. Tang, A. Zhang, H. Zhan, M. Lu, X. Chen, S. Yang, Z. Dong, Y. Wang, H. Liu, L. Zhao, L. Huang, Y. Li, L. Wu, Z. Chen, Y. Luo, D. Liu, P. Zhao, K. Lin, J. C. Wu, and S. Zhao, “Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging,” Nature Medicine , vol. 30, no. 5, p. 1471–1480, 2024
2024
Cited alongside, same era.
J. Ma, Y. Zhang, S. Gu, C. Ge, S. Mae, A. Young, C. Zhu, X. Yang, K. Meng, Z. Huang, F. Zhang, Y. Pan, S. Huang, J. Wang, M. Sun, R. Zhang, D. Jia, J. W. Choi, N. Alves, B. de Wilde, G. Koehler, H. Lai, E. Wang, M. Wiesenfarth, Q. Zhu, G. Dong, J. He, J. He, H. Yang, B. Huang, M. Lyu, Y. Ma, H. Guo, W. Xu, K. Maier-Hein, Y. Wu, and B. Wang, “Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the flare22 challenge,” The Lancet Digital Health , vol. 6, no. 11, p. e815–e826, 2024
2024
Cited alongside, same era.
S. Gatidis, M. Früh, M. P. Fabritius, S. Gu, K. Nikolaou, C. L. Fougère, J. Ye, J. He, Y. Peng, L. Bi, J. Ma, B. Wang, J. Zhang, Y. Huang, L. Heiliger, Z. Marinov, R. Stiefelhagen, J. Egger, J. Kleesiek, L. Sibille, L. Xiang, S. Bendazzoli, M. Astaraki, M. Ingrisch, C. C. Cyran, and T. Küstner, “Results from the autopet challenge on fully automated lesion segmentation in oncologic pet/ct imaging,” Nature Machine Intelligence , vol. 6, no. 11, p. 1396–1405, 2024
2024
Cited alongside, same era.
Later among the works it cites.
Z. Yan, W. Sun, R. Zhou, Z. Yuan, K. Zhang, Y. Li, T. Liu, Q. Li, X. Li, L. He, and L. Sun, “Biomedical sam 2: Segment anything in biomedical images and videos,” 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
A. Pfefferle, L. Purucker, and F. Hutter, “Daft: Data-aware fine-tuning of foundation models for efficient and effective medical image segmentation,” in CVPR 2024: Segment Anything In Medical Images On Laptop , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
C. Varghese, E. M. Harrison, G. O’Grady, and E. J. Topol, “Artificial intelligence in surgery,” Nature Medicine , vol. 30, no. 5, pp. 1257–1268, 2024
2024
Later among the works it cites.
T. Zhao, Y. Gu, J. Yang, N. Usuyama, H. H. Lee, S. Kiblawi, T. Naumann, J. Gao, A. Crabtree, J. Abel, C. Moung-Wen, B. Piening, C. Bifulco, M. Wei, H. Poon, and S. Wang, “A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities,” Nature Methods , 2024
2024
Later among the works it cites.
K. Bartnik, T. Bartczak, M. Krzyziński, K. Korzeniowski, K. Lamparski, P. Węgrzyn, E. Lam, M. Bartkowiak, T. Wróblewski, K. Mech, M. Januszewicz, and P. Biecek, “Waw-tace: A hepatocellular carcinoma multiphase ct dataset with segmentations, radiomics features, and clinical data,” Radiology: Artificial Intelligence , vol. 6, no. 6, p. e240296, 2024
2024
Later among the works it cites.
J. Su, M. Ahmed, Y. Lu, S. Pan, W. Bo, and Y. Liu, “Roformer: Enhanced transformer with rotary position embedding,” Neurocomputing , vol. 568, p. 127063, 2024
2024
Later among the works it cites.
W. Khan, S. Leem, K. B. See, J. K. Wong, S. Zhang, and R. Fang, “A comprehensive survey of foundation models in medicine,” IEEE Reviews in Biomedical Engineering , 2025
2025
Closest in time.
J. Wu, Z. Wang, M. Hong, W. Ji, H. Fu, Y. Xu, M. Xu, and Y. Jin, “Medical sam adapter: Adapting segment anything model for medical image segmentation,” Medical Image Analysis , p. 103547, 2025
2025
Closest in time.
2025
Closest in time.
N. Ravi, V. Gabeur, Y.-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. Rädle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V. Alwala, N. Carion, C.-Y. Wu, R. Girshick, P. Dollár, and C. Feichtenhofer, “Sam 2: Segment anything in images and videos,” in International Conference on Learning Representations , 2025
2025
Closest in time.
A. Archit, L. Freckmann, S. Nair, N. Khalid, P. Hilt, V. Rajashekar, M. Freitag, C. Teuber, G. Buckley, S. von Haaren, S. Gupta, A. Dengel, S. Ahmed, and C. Pape, “Segment anything for microscopy,” Nature Methods , 2025
2025
Closest in time.
M. Lou, H. Ying, X. Liu, H.-Y. Zhou, Y. Zhang, and Y. Yu, “Sdr-former: A siamese dual-resolution transformer for liver lesion classification using 3d multi-phase imaging,” Neural Networks , p. 107228, 2025
2025
Closest in time.