419 research outputs found
A Review on Deep Learning in Medical Image Reconstruction
Medical imaging is crucial in modern clinics to guide the diagnosis and
treatment of diseases. Medical image reconstruction is one of the most
fundamental and important components of medical imaging, whose major objective
is to acquire high-quality medical images for clinical usage at the minimal
cost and risk to the patients. Mathematical models in medical image
reconstruction or, more generally, image restoration in computer vision, have
been playing a prominent role. Earlier mathematical models are mostly designed
by human knowledge or hypothesis on the image to be reconstructed, and we shall
call these models handcrafted models. Later, handcrafted plus data-driven
modeling started to emerge which still mostly relies on human designs, while
part of the model is learned from the observed data. More recently, as more
data and computation resources are made available, deep learning based models
(or deep models) pushed the data-driven modeling to the extreme where the
models are mostly based on learning with minimal human designs. Both
handcrafted and data-driven modeling have their own advantages and
disadvantages. One of the major research trends in medical imaging is to
combine handcrafted modeling with deep modeling so that we can enjoy benefits
from both approaches. The major part of this article is to provide a conceptual
review of some recent works on deep modeling from the unrolling dynamics
viewpoint. This viewpoint stimulates new designs of neural network
architectures with inspirations from optimization algorithms and numerical
differential equations. Given the popularity of deep modeling, there are still
vast remaining challenges in the field, as well as opportunities which we shall
discuss at the end of this article.Comment: 31 pages, 6 figures. Survey pape
The Adaptive Quadratic Linear Unit (AQuLU): Adaptive Non Monotonic Piecewise Activation Function
The activation function plays a key role in influencing the performance and training dynamics of neural networks. There are hundreds of activation functions widely used as rectified linear units (ReLUs), but most of them are applied to complex and large neural networks, which often have gradient explosion and vanishing gradient problems. By studying a variety of non-monotonic activation functions, we propose a method to construct a non-monotonic activation function, x·Φ(x), with Φ(x) [0, 1]. With the hardening treatment of Φ(x), we propose an adaptive non-monotonic segmented activation function, called the adaptive quadratic linear unit, abbreviated as AQuLU, which ensures the sparsity of the input data and improves training efficiency. In image classification based on different state-of-the-art neural network architectures, the performance of AQuLUs has significant advantages for more complex and deeper architectures with various activation functions. The ablation experimental study further validates the compatibility and stability of AQuLUs with different depths, complexities, optimizers, learning rates, and batch sizes. We thus demonstrate the high efficiency, robustness, and simplicity of AQuLUs
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Purpose: To develop a deep learning approach to de-noise optical coherence
tomography (OCT) B-scans of the optic nerve head (ONH).
Methods: Volume scans consisting of 97 horizontal B-scans were acquired
through the center of the ONH using a commercial OCT device (Spectralis) for
both eyes of 20 subjects. For each eye, single-frame (without signal
averaging), and multi-frame (75x signal averaging) volume scans were obtained.
A custom deep learning network was then designed and trained with 2,328 "clean
B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean
B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance
of the de-noising algorithm was assessed qualitatively, and quantitatively on
1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio
(CNR), and mean structural similarity index metrics (MSSIM).
Results: The proposed algorithm successfully denoised unseen single-frame OCT
B-scans. The denoised B-scans were qualitatively similar to their corresponding
multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR
increased from dB (single-frame) to dB
(denoised). For all the ONH tissues, the mean CNR increased from (single-frame) to (denoised). The MSSIM increased from
(single frame) to (denoised) when compared with
the corresponding multi-frame B-scans.
Conclusions: Our deep learning algorithm can denoise a single-frame OCT
B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior
quality OCT B-scans with reduced scanning times and minimal patient discomfort
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