& Gupta, R. (2020). SDCT-AuxNetθ : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis. In Medical Image Analysis (Vol. 61, p. 101661). Elsevier BV. https://doi.org/10.1016/j.media.2020.101661 |
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| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 |
Other Publications Using This DataThe following publications are recommended by the data submitters that may be useful to researchers utilizing this collection: Gupta, R., Gehlot, S., & Gupta, A. (2022). C-NMC: B-lineage acute lymphoblastic leukaemia: A blood cancer dataset. Medical Engineering & Physics, 103. doi: https://doi.org/10.1016/j.medengphy.2022.103793 - Goswami, S., Mehta, S., Sahrawat, D., Gupta, A., & Gupta, R. (2020). Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer (Version 2). ICLR workshop on Affordable AI in healthcare, 2020. arXiv preprint https://doi.org/10.48550/arXiv.2003.03295
Gehlot, S., Gupta, A., & Gupta, R. (2021). A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis. Medical image analysis, 72, 102099. doi:https://doi.org/10.1016/j.media.2021.102099 - Anubha Gupta, Rahul Duggal, Shiv Gehlot, Ritu Gupta, Anvit Mangal, Lalit Kumar, Nisarg Thakkar, and Devprakash Satpathy, "GCTI-SN: Geometry-Inspired Chemical and Tissue Invariant Stain Normalization of Microscopic Medical Images," Medical Image Analysis, vol. 65, Oct 2020. DOI: https://doi.org/10.1016/j.media.2020.101788
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