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A new semi-supervised self-training method for lung cancer prediction., , , , , , and . CoRR, (2020)CASPIANET++: A multidimensional Channel-Spatial Asymmetric attention network with Noisy Student Curriculum Learning paradigm for brain tumor segmentation., , , and . Comput. Biol. Medicine, (2021)Increasing cancer detection yield of breast MRI using a new CAD scheme of mammograms., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 97850R. SPIE, (2016)Automated feature selection in neuroevolution., , , and . Evol. Intell., 1 (4): 271-292 (2009)Histogram analysis of CT scans for patients with post-mastectomy lymphedema., , , , , and . ICIP, page 1585-1588. IEEE, (2011)A new application of electrical impedance spectroscopy for measuring glucose metabolism: a phantom study., , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 9417 of SPIE Proceedings, page 94172K. SPIE, (2015)An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology., , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 978521. SPIE, (2016)Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 978520. SPIE, (2016)A new mass classification system derived from multiple features and a trained MLP model., , and . Medical Imaging: Computer-Aided Diagnosis, volume 9035 of SPIE Proceedings, page 90351P. SPIE, (2014)A new Fourier transform based CBIR scheme for mammographic mass classification: a preliminary invariance assessment., , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 941420. SPIE, (2015)