Proceedings ArticleDOI
Analysis of Deep Neural Network Architectures and Similarity Metrics for Low-Dose CT Reconstruction
Jonas Brusokas,Linas Petkevicius +1 more
- pp 1-6
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TLDR
Experimental results showed that selection of loss function can have significant impact on model performance, with the GSSIM metric outperforming other contemporary metrics SSIM, MSSSIM and MSE.Abstract:
Computed tomography (CT) is a widely used imaging technique in the medical field. During CT procedures patients are exposed to high amounts of radiation, posing a tangible threat to their health. Developed low-dose procedures lower exposure but produce noise and artifacts in images. To improve diagnostic accuracy, deep learning techniques are proposed to remove noises and artifacts from low-dose images. In this paper, the performance of several neural network architectures and similarity metrics as loss functions for low-dose CT image reconstruction are analyzed. Experimental results showed that selection of loss function can have significant impact on model performance, with the GSSIM metric outperforming other contemporary metrics SSIM, MSSSIM and MSE. Experiments were conducted using open-access and local cancer research institution data.read more
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Journal ArticleDOI
The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
TL;DR: In this paper , the authors performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction.
Proceedings ArticleDOI
Analysis of Information Compression of Medical Images for Survival Models
Ieva Jonaityte,Linas Petkevicius +1 more
TL;DR: In this article, the authors investigate the methods for extraction of significant information from medical breast cancer images for survival analysis using unsupervised learning models for information compression to bottlenecks via convolutional neural networks and autoencoders.
References
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Book ChapterDOI
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Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
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Generative Adversarial Networks
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
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Journal ArticleDOI
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