scholarly journals Deep learning-enabled whole slide imaging (DeepWSI): oil-immersion quality using dry objectives, longer depth of field, higher system throughput, and better functionality

2021 ◽  
Author(s):  
Chengfei Guo ◽  
Shaowei Jiang ◽  
Liming Yang ◽  
Pengming Song ◽  
Tianbo Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Throughput ◽  
Depth Of Field ◽  
Whole Slide Imaging ◽  
Oil Immersion

scholarly journals Transform- and multi-domain deep learning for single-frame rapid autofocusing in whole slide imaging

2018 ◽  
Vol 9 (4) ◽  
pp. 1601 ◽  
Author(s):  
Shaowei Jiang ◽  
Jun Liao ◽  
Zichao Bian ◽  
Kaikai Guo ◽  
Yongbing Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Whole Slide Imaging ◽  
Single Frame

scholarly journals Web-based oil immersion whole slide imaging increases efficiency and clinical team satisfaction in hematopathology tumor board

2014 ◽  
Vol 5 (1) ◽  
pp. 41 ◽  
Author(s):  
MohamedE Salama ◽  
ZhongchuanWill Chen ◽  
Jessica Kohan ◽  
JerryW Hussong ◽  
SherrieL Perkins
Keyword(s):  
Tumor Board ◽  
Clinical Team ◽  
Whole Slide Imaging ◽  
Web Based ◽  
Team Satisfaction ◽  
Oil Immersion

Extended depth of field photoacoustic microscopy using image fusion based on deep learning

2021 ◽  
Author(s):  
Sihang Li ◽  
Zhuangzhuang Wang ◽  
Chenghao Gu ◽  
Xianlin Song
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Depth Of Field ◽  
Photoacoustic Microscopy ◽  
Extended Depth Of Field

scholarly journals A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-enabled IoT

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6555
Author(s):  
Radwa Ahmed Osman ◽  
Sherine Nagy Saleh ◽  
Yasmine N. M. Saleh
Keyword(s):  
Energy Efficiency ◽  
Deep Learning ◽  
Optimization Technique ◽  
Data Communication ◽  
Learning Model ◽  
Base Station ◽  
System Throughput ◽  
Interference Avoidance ◽  
5G Networks ◽  
Deep Learning Model

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.

scholarly journals Low-cost whole slide imaging system withsingle-shot autofocusing based on colormultiplexed illumination and deep learning

2021 ◽  
Author(s):  
Kaifa Xin ◽  
Shaowei Jiang ◽  
xu chen ◽  
Yonghong He ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Low Cost ◽  
Whole Slide Imaging

scholarly journals Toward More Reliable Deep Learning-Based Link Adaptation for WiFi 6

2020 ◽  
Author(s):  
Mostafa Hussien
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Adaptive Modulation ◽  
Selection Criterion ◽  
Link Adaptation ◽  
Classification Problem ◽  
System Throughput ◽  
Adaptive Modulation And Coding ◽  
Proposed Model ◽  
Multi Class Classification

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in model failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, retransmission is required, which largely degrades the system throughput. To address this issue, we formulate the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed formulation allows more control over what the model predicts in failure cases. In this context, we propose a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset is generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. It is shown that some criteria for dataset selection consistently behave better than others. To confirm the performance, we applied the proposed model for adapting the IEEE 802.11ax standard in outdoor propagation scenarios. The simulation results show that the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions. Finally, we propose an optimal subdataset selection criterion.

Whole Slide Imaging: Deep Learning and Artificial Intelligence

2021 ◽  
pp. 223-236
Author(s):  
Asmaa Aljuhani ◽  
Arunima Srivastava ◽  
James P. Cronin ◽  
Jany Chan ◽  
Raghu Machiraju ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Whole Slide Imaging

scholarly journals Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery

Optica ◽  
2018 ◽  
Vol 5 (6) ◽  
pp. 704 ◽  
Author(s):  
Yichen Wu ◽  
Yair Rivenson ◽  
Yibo Zhang ◽  
Zhensong Wei ◽  
Harun Günaydin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Depth Of Field ◽  
Phase Recovery ◽  
Holographic Imaging ◽  
Extended Depth Of Field

scholarly journals Towards More Reliable Deep Learning-Based Link Adaptation for WiFi 6

2021 ◽  
Author(s):  
Mostafa Hussien
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
Adaptive Modulation ◽  
Link Adaptation ◽  
Classification Problem ◽  
System Throughput ◽  
Adaptive Modulation And Coding ◽  
Coding Scheme ◽  
Proposed Model ◽  
Multi Class Classification

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, a retransmission is required, which largely degrades the system throughput. To address this issue, we model the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed modeling allows more control over what the model predicts in failure cases. We also design a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset has been generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. The proposed model adapts the IEEE 802.11ax communications standard in outdoor scenarios. The simulation results show the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions.

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