86 research outputs found
Urinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/1/mp4498.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/2/mp4498_am.pd
Detection of urinary bladder mass in CT urography with SPAN
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134872/1/mp2503.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134872/2/mp2503_am.pd
Deep learning in medical imaging and radiation therapy
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd
Urinary bladder cancer staging in CT urography using machine learning
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139956/1/mp12510.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/139956/2/mp12510_am.pd
Ureter tracking and segmentation in CT urography (CTU) using COMPASS
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134875/1/mp1412_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134875/2/mp1412.pd
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AFE-GAN: Synthesizing Electrocardiograms with Atrial Fibrillation Characteristics Using Generative Adversarial Networks *
Labeled ECG data in diseased state are, however, relatively scarce due to various concerns including patient privacy and low prevalence. We propose the first study in its kind that synthesizes atrial fibrillation (AF)-like ECG signals from normal ECG signals using the AFE-GAN, a generative adversarial network. Our AFE-GAN adjusts both beat morphology and rhythm variability when generating the atrial fibrillation-like ECG signals. Two publicly available arrhythmia detectors classified 72.4% and 77.2% of our generated signals as AF in a four-class (normal, AF, other abnormal, noisy) classification. This work shows the feasibility to synthesize abnormal ECG signals from normal ECG signals.Clinical significance - The AF ECG signal generated with our AFE-GAN has the potential to be used as training materials for health practitioners or be used as class-balance supplements for training automatic AF detectors
Deepâ learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147844/1/mp13326.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147844/2/mp13326_am.pd
Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets
Uâ Net based deep learning bladder segmentation in CT urography
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149261/1/mp13438.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149261/2/mp13438_am.pd
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