Fetching the paper…
Reading the bibliography…
Recent advancements in foundation models have shown significant potential in medical image analysis.
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.
The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Armato III, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al., 2011 · 2011
Earlier work this paper cites.
Validation of anatomical landmarks-based registration for image-guided surgery: an in-vitro study
Sun, Y., Luebbers, H.T., Agbaje, J.O., Schepers, S., Vrielinck, L., Lambrichts, I., Politis, C., 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J., 2015 · 2015
Earlier work this paper cites.
R-fcn: Object detection via region-based fully convolutional networks
Dai, J., Li, Y., He, K., Sun, J., 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788
Redmon, J., Divvala, S., Girshick, R., Farhadi, A., 2016 · 2016
Earlier work this paper cites.
Mask r-cnn, in: Proceedings of the IEEE international conference on computer vision, pp. 2961–2969
He, K., Gkioxari, G., Dollár, P., Girshick, R., 2017 · 2017
Earlier work this paper cites.
A curated mammography data set for use in computer-aided detection and diagnosis research
Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L., 2017 · 2017
Earlier work this paper cites.
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Vallieres, M., Kay-Rivest, E., Perrin, L.J., Liem, X., Furstoss, C., Aerts, H.J., Khaouam, N., Nguyen-Tan, P.F., Wang, C.S., Sultanem, K., et al., 2017 · 2017
Earlier work this paper cites.
Imaging and clinical data archive for head and neck squamous cell carcinoma patients treated with radiotherapy
Grossberg, A.J., Mohamed, A.S., Elhalawani, H., Bennett, W.C., Smith, K.E., Nolan, T.S., Williams, B., Chamchod, S., Heukelom, J., Kantor, M.E., et al., 2018 · 2018
Earlier work this paper cites.
Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen ct scans
Humpire-Mamani, G.E., Setio, A.A.A., Van Ginneken, B., Jacobs, C., 2018 · 2018
Earlier work this paper cites.
Radiomic biomarkers to refine risk models for distant metastasis in hpv-related oropharyngeal carcinoma
Kwan, J.Y.Y., Su, J., Huang, S.H., Ghoraie, L.S., Xu, W., Chan, B., Yip, K.W., Giuliani, M., Bayley, A., Kim, J., et al., 2018 · 2018
Earlier work this paper cites.
Fast multiple landmark localisation using a patch-based iterative network, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, Springer. pp. 563–571
Li, Y., Alansary, A., Cerrolaza, J.J., Khanal, B., Sinclair, M., Matthew, J., Gupta, C., Knight, C., Kainz, B., Rueckert, D., 2018 · 2018
Earlier work this paper cites.
Deepigeos: a deep interactive geodesic framework for medical image segmentation
Wang, G., Zuluaga, M.A., Li, W., Pratt, R., Patel, P.A., Aertsen, M., Doel, T., David, A.L., Deprest, J., Ourselin, S., et al., 2018 · 2018
Earlier work this paper cites.
Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning
Yan, K., Wang, X., Lu, L., Summers, R.M., 2018 · 2018
Earlier work this paper cites.
Automatic nodule detection for lung cancer in ct images: A review
Zhang, G., Jiang, S., Yang, Z., Gong, L., Ma, X., Zhou, Z., Bao, C., Liu, Q., 2018 · 2018
Earlier work this paper cites.
Slowfast networks for video recognition, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 6202–6211
Feichtenhofer, C., Fan, H., Malik, J., He, K., 2019 · 2019
Earlier work this paper cites.
Deepigeos-v2: Deep interactive segmentation of multiple organs from head and neck images with lightweight cnns, in: Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention: International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings 4, Springer. pp. 61–69
Lei, W., Wang, H., Gu, R., Zhang, S., Zhang, S., Wang, G., 2019 · 2019
Earlier work this paper cites.
Multicenter trial of [18f] fluorodeoxyglucose positron emission tomography/computed tomography staging of head and neck cancer and negative predictive value and surgical impact in the n0 neck: results from acrin 6685
Lowe, V.J., Duan, F., Subramaniam, R.M., Sicks, J.D., Romanoff, J., Bartel, T., Yu, J.Q.M., Nussenbaum, B., Richmon, J., Arnold, C.D., et al., 2019 · 2019
Earlier work this paper cites.
Efficient multiple organ localization in ct image using 3d region proposal network
Xu, X., Zhou, F., Liu, B., Fu, D., Bai, X., 2019 · 2019
Earlier work this paper cites.
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge
Heller, N., Isensee, F., Maier-Hein, K.H., Hou, X., Xie, C., Li, F., Nan, Y., Mu, G., Lin, Z., Han, M., et al., 2020 · 2020
Earlier work this paper cites.
Retina u-net: Embarrassingly simple exploitation of segmentation supervision for medical object detection, in: Machine Learning for Health Workshop, PMLR. pp. 171–183
Jaeger, P.F., Kohl, S.A., Bickelhaupt, S., Isensee, F., Kuder, T.A., Schlemmer, H.P., Maier-Hein, K.H., 2020 · 2020
Cited alongside, same era.
Deep reinforcement learning for organ localization in ct, in: Medical Imaging with Deep Learning, PMLR. pp. 544–554
Navarro, F., Sekuboyina, A., Waldmannstetter, D., Peeken, J.C., Combs, S.E., Menze, B.H., 2020 · 2020
Cited alongside, same era.
nndetection: a self-configuring method for medical object detection, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part V 24, Springer. pp. 530–539
Baumgartner, M., Jäger, P.F., Isensee, F., Maier-Hein, K.H., 2021 · 2021
Cited alongside, same era.
Toward human intervention-free clinical diagnosis of intracranial aneurysm via deep neural network
Bo, Z.H., Qiao, H., Tian, C., Guo, Y., Li, W., Liang, T., Li, D., Liao, D., Zeng, X., Mei, L., et al., 2021 · 2021
Cited alongside, same era.
A unified 3d framework for organs-at-risk localization and segmentation for radiation therapy planning, in: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE. pp. 1544–1547
Navarro, F., Sasahara, G., Shit, S., Sekuboyina, A., Ezhov, I., Peeken, J.C., Combs, S.E., Menze, B.H., 2022 · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al., 2022 · 2022
Later among the works it cites.
Medical image understanding with pretrained vision language models: A comprehensive study
Qin, Z., Yi, H., Lao, Q., Li, K., 2022 · 2022
Later among the works it cites.
Glipv2: Unifying localization and vision-language understanding
Zhang, H., Zhang, P., Hu, X., Chen, Y.C., Li, L., Dai, X., Wang, L., Yuan, L., Hwang, J.N., Gao, J., 2022 · 2022
Later among the works it cites.
The liver tumor segmentation benchmark (lits)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Emerging properties in self-supervised vision transformers, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 9650–9660
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021 · 2021
Cited alongside, same era.
Fast and accurate craniomaxillofacial landmark detection via 3d faster r-cnn
Chen, X., Lian, C., Deng, H.H., Kuang, T., Lin, H.Y., Xiao, D., Gateno, J., Shen, D., Xia, J.J., Yap, P.T., 2021 · 2021
Cited alongside, same era.
Cascaded regression neural nets for kidney localization and segmentation-free volume estimation
Hussain, M.A., Hamarneh, G., Garbi, R., 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Contrastive learning of relative position regression for one-shot object localization in 3d medical images, in: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II 24, Springer. pp. 155–165
Lei, W., Xu, W., Gu, R., Fu, H., Zhang, S., Zhang, S., Wang, G., 2021b · 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.
Study of thoracic ct in covid-19: the stoic project
Revel, M.P., Boussouar, S., de Margerie-Mellon, C., Saab, I., Lapotre, T., Mompoint, D., Chassagnon, G., Milon, A., Lederlin, M., Bennani, S., et al., 2021 · 2021
Cited alongside, same era.
Detco: Unsupervised contrastive learning for object detection, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 8392–8401
Xie, E., Ding, J., Wang, W., Zhan, X., Xu, H., Sun, P., Li, Z., Luo, P., 2021 · 2021
Cited alongside, same era.
Bilic, P., Christ, P., Li, H.B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G.E.H., Chartrand, G., et al., 2023 · 2023
Closest in time.
Universeg: Universal medical image segmentation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21438–21451
Butoi, V.I., Ortiz, J.J.G., Ma, T., Sabuncu, M.R., Guttag, J., Dalca, A.V., 2023 · 2023
Closest in time.
Sam on medical images: A comprehensive study on three prompt modes
Cheng, D., Qin, Z., Jiang, Z., Zhang, S., Lao, Q., Li, K., 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.
Clip-driven universal model for organ segmentation and tumor detection
Liu, J., Zhang, Y., Chen, J.N., Xiao, J., Lu, Y., Landman, B.A., Yuan, Y., Yuille, A., Tang, Y., Zhou, Z., 2023 · 2023
Closest in time.
Segment anything model for medical image analysis: an experimental study
Mazurowski, M.A., Dong, H., Gu, H., Yang, J., Konz, N., Zhang, Y., 2023 · 2023
Closest in time.
Foundation models for generalist medical artificial intelligence
Moor, M., Banerjee, O., Abad, Z.S.H., Krumholz, H.M., Leskovec, J., Topol, E.J., Rajpurkar, P., 2023 · 2023
Closest in time.
Medical image understanding with pretrained vision language models: A comprehensive study, in: The Eleventh International Conference on Learning Representations
Qin, Z., Yi, H.H., Lao, Q., Li, K., 2023 · 2023
Closest in time.
Virchow: A million-slide digital pathology foundation model
Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Liu, S., Mathieu, P., Eck, A.v., Lee, D., Viret, J., et al., 2023 · 2023
Closest in time.
Multi-target landmark detection with incomplete images via reinforcement learning and shape prior embedding
Wan, K., Li, L., Jia, D., Gao, S., Qian, W., Wu, Y., Lin, H., Mu, X., Gao, X., Wang, S., et al., 2023 · 2023
Closest in time.
Wang, G., Wu, J., Luo, X., Liu, X., Li, K., Zhang, S., 2023 · 2023
Closest in time.
Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images
Wasserthal, J., Breit, H.C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D.T., Cyriac, J., Yang, S., et al., 2023 · 2023
Closest in time.
Medical sam adapter: Adapting segment anything model for medical image segmentation
Wu, J., Fu, R., Fang, H., Liu, Y., Wang, Z., Xu, Y., Jin, Y., Arbel, T., 2023 · 2023
Closest in time.
Customized segment anything model for medical image segmentation
Zhang, K., Liu, D., 2023 · 2023
Closest in time.
On the challenges and perspectives of foundation models for medical image analysis
Zhang, S., Metaxas, D., 2023 · 2023
Closest in time.
How segment anything model (sam) boost medical image segmentation?
Zhang, Y., Jiao, R., 2023 · 2023
Closest in time.
A foundation model for generalizable disease detection from retinal images
Zhou, Y., Chia, M.A., Wagner, S.K., Ayhan, M.S., Williamson, D.J., Struyven, R.R., Liu, T., Xu, M., Lozano, M.G., Woodward-Court, P., et al., 2023 · 2023
Closest in time.
Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B., 2024 · 2024
Closest in time.