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On instabilities of deep learning in image reconstruction and the ...
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WebSep 16, 2024 · A new method employing deep learning to recover high-quality images from sparse or limited-view optoacoustic scans has been … Given the availability of well-established deep learning models from computer vision applications, one of the most straightforward ways of applying deep learning for tomographic reconstruction is to reduce image artefacts as a post-processing step using image domain deep networks (step 4 in Fig. 2). For example, … See more Unfortunately, image-domain learning approaches often suffer from image blurring, especially when the training data is not sufficient. This … See more Rather than explicitly mapping each iterative step to a layer of an unrolled neural network, model-based and/or plug-and-play approaches incorporate a deep neural network as a prior term in the iterative … See more To mitigate the limitations of the domain transform approaches, some networks embed an analytic transform such as the Radon transform and the Fourier transform as imaging-physics-based knowledge inside the … See more A number of groups explored directly learning a tomographic mapping from sensor data to an underlying image (steps 2 and 3 in Fig. 2). With the automated transform by manifold approximation (AUTOMAP) … See more WebApr 12, 2024 · The models developed are based on deep learning convolutional neural networks and transfer learning, that enable an accurate automated detection of carotid calcifications, with a recall of 0.82 and a specificity of 0.97. ... Traditional approaches for CAC detection are doppler ultrasound screening and angiography computerized … edith eyraud