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4D radiomics in dynamic contrast-enhanced MRI: prediction of pathological complete response and systemic recurrence in triple-negative breast cancer.

, , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 12033 из SPIE Proceedings, SPIE, (2022)

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Johannes Marcour

Hans-Ernst Marcolin

Monika Marcotty

Carlos Javier Marcos Navas

Massimiliano Marcon

 

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Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors., , , , и . Medical Imaging: Image Processing, том 12032 из SPIE Proceedings, SPIE, (2022)End-to-end mammographic breast density quantification with deep learning: preliminary study on simulated mammograms., , , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 12465 из SPIE Proceedings, SPIE, (2023)Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation., , , , , , , , , и . Medical Image Anal., (2021)Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images., , , и . Medical Imaging: Computer-Aided Diagnosis, том 10950 из SPIE Proceedings, стр. 109500J. SPIE, (2019)4D radiomics in dynamic contrast-enhanced MRI: prediction of pathological complete response and systemic recurrence in triple-negative breast cancer., , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 12033 из SPIE Proceedings, SPIE, (2022)Automatic estimation of glandular tissue loss due to limited reconstruction voxel size in tomographic images of the breast., , , , , и . IWBI, том 10718 из SPIE Proceedings, стр. 107181G. SPIE, (2018)