scholarly journals Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

2019 ◽  
Vol 13 (3) ◽  
pp. 283-357
Author(s):  
Michael T. McCann ◽  
Michael Unser
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Deep Neural Networks ◽  
Biomedical Image

scholarly journals Paired and Unpaired Deep Generative Models on Multimodal Retinal Image Reconstruction

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 45 ◽  
Author(s):  
Álvaro Hervella ◽  
José Rouco ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Fluorescein Angiography ◽  
Retinal Image ◽  
Deep Neural Networks ◽  
Generative Models ◽  
Retinal Images

This work explores the use of paired and unpaired data for training deep neural networks in the multimodal reconstruction of retinal images. Particularly, we focus on the reconstruction of fluorescein angiography from retinography, which are two complementary representations of the eye fundus. The performed experiments allow to compare the paired and unpaired alternatives.

Biomedical image augmentation using Augmentor

2019 ◽  
Vol 35 (21) ◽  
pp. 4522-4524 ◽  
Author(s):  
Marcus D Bloice ◽  
Peter M Roth ◽  
Andreas Holzinger
Keyword(s):  
Neural Networks ◽  
Software Package ◽  
Biomedical Imaging ◽  
Deep Neural Networks ◽  
Source Code ◽  
Imaging Features ◽  
Software Library ◽  
Biomedical Image ◽  
To Come ◽  
Python Package

Abstract Motivation Image augmentation is a frequently used technique in computer vision and has been seeing increased interest since the popularity of deep learning. Its usefulness is becoming more and more recognized due to deep neural networks requiring larger amounts of data to train, and because in certain fields, such as biomedical imaging, large amounts of labelled data are difficult to come by or expensive to produce. In biomedical imaging, features specific to this domain need to be addressed. Results Here we present the Augmentor software package for image augmentation. It provides a stochastic, pipeline-based approach to image augmentation with a number of features that are relevant to biomedical imaging, such as z-stack augmentation and randomized elastic distortions. The software has been designed to be highly extensible meaning an operation that might be specific to a highly specialized task can easily be added to the library, even at runtime. Although it has been designed as a general software library, it has features that are particularly relevant to biomedical imaging and the techniques required for this domain. Availability and implementation Augmentor is a Python package made available under the terms of the MIT licence. Source code can be found on GitHub under https://github.com/mdbloice/Augmentor and installation is via the pip package manager (A Julia version of the package, developed in parallel by Christof Stocker, is also available under https://github.com/Evizero/Augmentor.jl).

scholarly journals Quantization of Deep Neural Networks for Accurate Edge Computing

2021 ◽  
Vol 17 (4) ◽  
pp. 1-11
Author(s):  
Wentao Chen ◽  
Hailong Qiu ◽  
Jian Zhuang ◽  
Chutong Zhang ◽  
Yu Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Automatic Speech Recognition ◽  
Deep Neural Networks ◽  
Biomedical Image ◽  
Wide Range ◽  
Improve Accuracy ◽  
Memory Reduction ◽  
Fully Connected

Deep neural networks have demonstrated their great potential in recent years, exceeding the performance of human experts in a wide range of applications. Due to their large sizes, however, compression techniques such as weight quantization and pruning are usually applied before they can be accommodated on the edge. It is generally believed that quantization leads to performance degradation, and plenty of existing works have explored quantization strategies aiming at minimum accuracy loss. In this paper, we argue that quantization, which essentially imposes regularization on weight representations, can sometimes help to improve accuracy. We conduct comprehensive experiments on three widely used applications: fully connected network for biomedical image segmentation, convolutional neural network for image classification on ImageNet, and recurrent neural network for automatic speech recognition, and experimental results show that quantization can improve the accuracy by 1%, 1.95%, 4.23% on the three applications respectively with 3.5x-6.4x memory reduction.

Anatomically aided PET image reconstruction using deep neural networks

2021 ◽  
Author(s):  
Zhaoheng Xie ◽  
Tiantian Li ◽  
Xuezhu Zhang ◽  
Wenyuan Qi ◽  
Evren Asma ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Reconstruction ◽  
Deep Neural Networks ◽  
Pet Image Reconstruction

scholarly journals A single latent channel is sufficient for biomedical image segmentation

2021 ◽  
Author(s):  
Andreas M Kist ◽  
Stephan Duerr ◽  
Anne Schuetzenberger ◽  
Marion Semmler
Keyword(s):  
Neural Networks ◽  
High Speed ◽  
Deep Neural Networks ◽  
Reconstruction Accuracy ◽  
Crucial Step ◽  
Biomedical Image ◽  
Latent Space ◽  
Fully Automatic ◽  
Highly Correlated ◽  
Game Changer

Glottis segmentation is a crucial step to quantify endoscopic footage in laryngeal high-speed videoendoscopy. Recent advances in using deep neural networks for glottis segmentation allow a fully automatic workflow. However, exact knowledge of integral parts of these segmentation deep neural networks remains unknown. Here, we show using systematic ablations that a single latent channel as bottleneck layer is sufficient for glottal area segmentation. We further show that the latent space is an abstraction of the glottal area segmentation relying on three spatially defined pixel subtypes. We provide evidence that the latent space is highly correlated with the glottal area waveform, can be encoded with four bits, and decoded using lean decoders while maintaining a high reconstruction accuracy. Our findings suggest that glottis segmentation is a task that can be highly optimized to gain very efficient and clinical applicable deep neural networks. In future, we believe that online deep learning-assisted monitoring is a game changer in laryngeal examinations.

scholarly journals Detecting failure modes in image reconstructions with interval neural network uncertainty

2021 ◽  
Author(s):  
Luis Oala ◽  
Cosmas Heiß ◽  
Jan Macdonald ◽  
Maximilian März ◽  
Gitta Kutyniok ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Reconstruction ◽  
Deep Neural Networks ◽  
Failure Modes ◽  
Network Models ◽  
Quantitative Detection ◽  
Neural Network Models ◽  
Post Hoc ◽  
Interval Neural Network

Abstract Purpose The quantitative detection of failure modes is important for making deep neural networks reliable and usable at scale. We consider three examples for common failure modes in image reconstruction and demonstrate the potential of uncertainty quantification as a fine-grained alarm system. Methods We propose a deterministic, modular and lightweight approach called Interval Neural Network (INN) that produces fast and easy to interpret uncertainty scores for deep neural networks. Importantly, INNs can be constructed post hoc for already trained prediction networks. We compare it against state-of-the-art baseline methods (MCDrop, ProbOut). Results We demonstrate on controlled, synthetic inverse problems the capacity of INNs to capture uncertainty due to noise as well as directional error information. On a real-world inverse problem with human CT scans, we can show that INNs produce uncertainty scores which improve the detection of all considered failure modes compared to the baseline methods. Conclusion Interval Neural Networks offer a promising tool to expose weaknesses of deep image reconstruction models and ultimately make them more reliable. The fact that they can be applied post hoc to equip already trained deep neural network models with uncertainty scores makes them particularly interesting for deployment.

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