Three‐dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations

Although deep neural networks (DNNs) are state-of-the-art artificial intelligence systems, it is unclear what insights, if any, they provide about human intelligence. We address this issue in the domain of visual perception. After briefly describing DNNs, we provide an overview of recent results comparing human visual representations and performance with those of DNNs. In many cases, DNNs acquire visual representations and processing strategies that are very different from those used by people. We conjecture that there are at least two factors preventing them from serving as better psychological models. First, DNNs are currently trained with impoverished data, such as data lacking important visual cues to three-dimensional structure, data lacking multisensory statistical regularities, and data in which stimuli are unconnected to an observer’s actions and goals. Second, DNNs typically lack adaptations to capacity limits, such as attentional mechanisms, visual working memory, and compressed mental representations biased toward preserving task-relevant abstractions.

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Developing Noise-Resistant Three-Dimensional Single Particle Tracking Using Deep Neural Networks

Analytical Chemistry ◽

10.1021/acs.analchem.8b01334 ◽

2018 ◽

Vol 90 (18) ◽

pp. 10748-10757 ◽

Cited By ~ 5

Author(s):

Yaning Zhong ◽

Chao Li ◽

Huiyang Zhou ◽

Gufeng Wang

Keyword(s):

Neural Networks ◽

Particle Tracking ◽

Single Particle ◽

Deep Neural Networks ◽

Three Dimensional ◽

Single Particle Tracking

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DeepEC: An error correction framework for dose prediction and organ segmentation using deep neural networks

International Journal of Intelligent Systems ◽

10.1002/int.22280 ◽

2020 ◽

Vol 35 (12) ◽

pp. 1987-2008 ◽

Cited By ~ 1

Author(s):

Han Wang ◽

Haixian Zhang ◽

Junjie Hu ◽

Ying Song ◽

Sen Bai ◽

...

Keyword(s):

Neural Networks ◽

Error Correction ◽

Deep Neural Networks ◽

Dose Prediction

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Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

Sensors ◽

10.3390/s21227521 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7521

Author(s):

Agnieszka Stankiewicz ◽

Tomasz Marciniak ◽

Adam Dabrowski ◽

Marcin Stopa ◽

Elzbieta Marciniak ◽

...

Keyword(s):

Neural Networks ◽

Optical Coherence Tomography ◽

Deep Neural Networks ◽

Three Dimensional ◽

Semantic Segmentation ◽

Optical Coherence ◽

The Neural Network ◽

Inner Limiting Membrane ◽

Segmentation Quality ◽

Oct Imaging

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

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