1 citations · 1 across the 1 of their papers we have counts for
4 papers
Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis
Koen Michielsen, Nikita Moriakov, Jonas Teuwen +1
Reconstruction of digital breast tomosynthesis is a challenging problem due to the limited angle data available in such systems. Due to memory limitations, deep learning-based meth…
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation
Joris van Vugt, Elena Marchiori, Ritse Mann +3
Computer-aided detection aims to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. DM exams are generated by devices from…
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
Nikita Moriakov, Koen Michielsen, Jonas Adler +3
Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular r…
Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network
Timothy de Moor, Alejandro Rodriguez-Ruiz, Albert Gubern Mérida +2
Computer-aided detection or decision support systems aim to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. Commonly su…