Fetching the paper…
Reading the bibliography…
Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses.
Sickles, E.A.: Breast calcifications: mammographic evaluation. Radiology
1986
Earlier work this paper cites.
DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics, 837–845 (1988)
1988
Earlier work this paper cites.
Guyatt, G.H., Rennie, D.: Users’ guides to the medical literature. Jama
1993
Earlier work this paper cites.
Ernster, V.L., Barclay, J.: Increases in ductal carcinoma in situ (dcis) of the breast in relation to mammography: a dilemma. JNCI Monographs
1997
Earlier work this paper cites.
Winchester, D.P., Jeske, J.M., Goldschmidt, R.A.: The diagnosis and management of ductal carcinoma in-situ of the breast. CA: a cancer journal for clinicians
2000
Earlier work this paper cites.
Lazarus, E., Mainiero, M.B., Schepps, B., Koelliker, S.L., Livingston, L.S.: Bi-rads lexicon for us and mammography: interobserver variability and positive predictive value. Radiology
2006
Earlier work this paper cites.
Kim, S.H., Seo, B.K., Lee, J., Kim, S.J., Cho, K.R., Lee, K.Y., Je, B.-K., Kim, H.Y., Kim, Y.-S., Lee, J.-H.: Correlation of ultrasound findings with histology, tumor grade, and biological markers in breast cancer. Acta oncologica
2008
Earlier work this paper cites.
Tse, G., Tan, P.H., Pang, A.L., Tang, A.P., Cheung, H.S.: Calcification in breast lesions: pathologists’ perspective. Journal of clinical pathology
2008
Earlier work this paper cites.
Ko, E.S., Lee, B.H., Kim, H.-A., Noh, W.-C., Kim, M.S., Lee, S.-A.: Triple-negative breast cancer: correlation between imaging and pathological findings. European radiology
2010
Earlier work this paper cites.
Pinder, S.E.: Ductal carcinoma in situ (dcis): pathological features, differential diagnosis, prognostic factors and specimen evaluation. Modern Pathology
2010
Earlier work this paper cites.
Wojcinski, S., Soliman, A.A., Schmidt, J., Makowski, L., Degenhardt, F., Hillemanns, P.: Sonographic features of triple-negative and non–triple-negative breast cancer. Journal of Ultrasound in Medicine
2012
Earlier work this paper cites.
Demetri-Lewis, A., Slanetz, P.J., Eisenberg, R.L.: Breast calcifications: the focal group. American Journal of Roentgenology
2012
Earlier work this paper cites.
Sickles, E.A.: Acr bi-rads® atlas, breast imaging reporting and data system. American College of Radiology., 39 (2013)
2013
Earlier work this paper cites.
Malin, B.A., Emam, K.E., O’Keefe, C.M.: Biomedical data privacy: problems, perspectives, and recent advances. Journal of the American medical informatics association
2013
Earlier work this paper cites.
Hoda, S.A., Brogi, E., Koerner, F.C., Rosen, P.P.: Rosen’s Breast Pathology: Fourth Edition, pp. 1–1400 (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Shen, S., Zhou, Y., Xu, Y., Zhang, B., Duan, X., Huang, R., Li, B., Shi, Y., Shao, Z., Liao, H.,
2015
Earlier work this paper cites.
Jones, C., Gannon, B., Wakai, A., O’Sullivan, R.: A systematic review of the cost of data collection for performance monitoring in hospitals. Systematic reviews
2015
Earlier work this paper cites.
Ohuchi, N., Suzuki, A., Sobue, T., Kawai, M., Yamamoto, S., Zheng, Y.-F., Shiono, Y.N., Saito, H., Kuriyama, S., Tohno, E.,
2016
Earlier work this paper cites.
Berg, W.A., Bandos, A.I., Mendelson, E.B., Lehrer, D., Jong, R.A., Pisano, E.D.: Ultrasound as the primary screening test for breast cancer: analysis from acrin 6666. Journal of the National Cancer Institute
2016
Earlier work this paper cites.
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
2016
Earlier work this paper cites.
Su, X., Lin, Q., Cui, C., Xu, W., Wei, Z., Fei, J., Li, L.: Non-calcified ductal carcinoma in situ of the breast: comparison of diagnostic accuracy of digital breast tomosynthesis, digital mammography, and ultrasonography. Breast Cancer
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
2017
Earlier work this paper cites.
De Fauw, J., Ledsam, J.R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D.,
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Gan-based synthetic medical image augmentation for increased cnn performance in liver lesion classification. Neurocomputing
2018
Cited alongside, same era.
Sood, R., Rositch, A.F., Shakoor, D., Ambinder, E., Pool, K.-L., Pollack, E., Mollura, D.J., Mullen, L.A., Harvey, S.C.: Ultrasound for breast cancer detection globally: a systematic review and meta-analysis. Journal of global oncology (2019)
2019
Cited alongside, same era.
Lin, Z., Lin, J., Zhu, L., Fu, H., Qin, J., Wang, L.: A new dataset and a baseline model for breast lesion detection in ultrasound videos. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 614–623 (2022). Springer
2022
Later among the works it cites.
Zhao, C., Xiao, M., Ma, L., Ye, X., Deng, J., Cui, L., Guo, F., Wu, M., Luo, B., Chen, Q.,
2022
Later among the works it cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A.,
2022
Later among the works it cites.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684–10695 (2022)
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Peng, Y., Tang, P.: Practical Breast Pathology Frequently Asked Questions: Frequently Asked Questions, (2019)
2019
Cited alongside, same era.
Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher, J.J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G.,
2019
Cited alongside, same era.
Price, W.N., Cohen, I.G.: Privacy in the age of medical big data. Nature medicine
2019
Cited alongside, same era.
Sandfort, V., Yan, K., Pickhardt, P.J., Summers, R.M.: Data augmentation using generative adversarial networks (cyclegan) to improve generalizability in ct segmentation tasks. Scientific reports
2019
Cited alongside, same era.
Gupta, A., Venkatesh, S., Chopra, S., Ledig, C.: Generative image translation for data augmentation of bone lesion pathology. In: International Conference on Medical Imaging with Deep Learning, pp. 225–235 (2019). PMLR
2019
Cited alongside, same era.
Zielonke, N., Gini, A., Jansen, E.E., Anttila, A., Segnan, N., Ponti, A., Veerus, P., Koning, H.J., Ravesteyn, N.T., Heijnsdijk, E.A.,
2020
Cited alongside, same era.
Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data in brief
2020
Cited alongside, same era.
Tan, P.H., Ellis, I., Allison, K., Brogi, E., Fox, S.B., Lakhani, S., Lazar, A.J., Morris, E.A., Sahin, A., Salgado, R., et al.: The 2019 who classification of tumours of the breast. Histopathology
2020
Cited alongside, same era.
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: International Conference on Learning Representations (2022)
2022
Later among the works it cites.
Logullo, A.F., Prigenzi, K.C., Nimir, C.C., Franco, A.F., Campos, M.S.: Breast microcalcifications: Past, present and future. Molecular and clinical oncology
2022
Later among the works it cites.
Chambon, P.J.M., Bluethgen, C., Langlotz, C., Chaudhari, A.: Adapting pretrained vision-language foundational models to medical imaging domains. In: NeurIPS 2022 Foundation Models for Decision Making Workshop (2022)
2022
Later among the works it cites.
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., Zhu, J.: Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems
2022
Later among the works it cites.
Siegel, R.L., Miller, K.D., Wagle, N.S., Jemal, A.,
2023
Later among the works it cites.
Chhikara, B.S., Parang, K.: Global cancer statistics 2022: the trends projection analysis. Chemical Biology Letters
2023
Later among the works it cites.
Ng, A.Y., Oberije, C.J., Ambrózay, É., Szabó, E., Serfőző, O., Karpati, E., Fox, G., Glocker, B., Morris, E.A., Forrai, G.,
2023
Later among the works it cites.
Zhang, Y., Kang, B., Hooi, B., Yan, S., Feng, J.: Deep long-tailed learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
2023
Later among the works it cites.
Neil, S.: Synthetic Data Could Be Better than Real Data
2023
Later among the works it cites.
Gao, C., Killeen, B.D., Hu, Y., Grupp, R.B., Taylor, R.H., Armand, M., Unberath, M.: Synthetic data accelerates the development of generalizable learning-based algorithms for x-ray image analysis. Nature Machine Intelligence
2023
Later among the works it cites.
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22500–22510 (2023)
2023
Later among the works it cites.
Cao, K., Xia, Y., Yao, J., Han, X., Lambert, L., Zhang, T., Tang, W., Jin, G., Jiang, H., Fang, X.,
2023
Later among the works it cites.
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.,
2023
Later among the works it cites.
Sun, S., Goldgof, G., Butte, A., Alaa, A.: Aligning synthetic medical images with clinical knowledge using human feedback. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)
2023
Later among the works it cites.
Hu, Q., Chen, Y., Xiao, J., Sun, S., Chen, J., Yuille, A.L., Zhou, Z.: Label-free liver tumor segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7422–7432 (2023)
2023
Later among the works it cites.
Sagers, L., Diao, J., Melas-Kyriazi, L., Groh, M., Rajpurkar, P., Adamson, A., Rotemberg, V., Daneshjou, R., Manrai, A.: Augmenting Medical Image Classifiers with Synthetic Data from Latent Diffusion Models
2023
Later among the works it cites.
Pinaya, W., Graham, M., Kerfoot, E., Tudosiu, P.-D., Dafflon, J., Fernandez, V., Sanchez, P., Wolleb, J., Costa, P., Patel, A., Chung, H., Zhao, C., Peng, W., Liu, Z., Mei, X., Lucena, O., Ye, J.C., Tsaftaris, S., Dogra, P., Cardoso, M.J.: Generative AI for Medical Imaging: Extending the MONAI Framework
2023
Later among the works it cites.
Ktena, I., Wiles, O., Albuquerque, I., Rebuffi, S.-A., Tanno, R., Roy, A.G., Azizi, S., Belgrave, D., Kohli, P., Cemgil, T., et al.: Generative models improve fairness of medical classifiers under distribution shifts. Nature Medicine, 1–8 (2024)
2024
Closest in time.