Stochastic arithmetic implementations of neural networks with in situ learning

2002 ◽  
Author(s):  
J.A. Dickson ◽  
R.D. McLeod ◽  
H.C. Card
Keyword(s):  
Neural Networks ◽  
Stochastic Arithmetic

scholarly journals Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks

2021 ◽  
Author(s):  
Xumeng Zhang ◽  
Jian Lu ◽  
Zhongrui Wang ◽  
Rui Wang ◽  
Jinsong Wei ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spiking Neural Networks

scholarly journals SATELLITE-DERIVED BATHYMETRY USING CONVOLUTIONAL NEURAL NETWORKS AND MULTISPECTRAL SENTINEL-2 IMAGES

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 201-207
Author(s):  
Y. A. Lumban-Gaol ◽  
K. A. Ohori ◽  
R. Y. Peters
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Window Size ◽  
Short Wave ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Linear Transform ◽  
Band Combinations ◽  
Sentinel 2

Abstract. Satellite-Derived Bathymetry (SDB) has been used in many applications related to coastal management. SDB can efficiently fill data gaps obtained from traditional measurements with echo sounding. However, it still requires numerous training data, which is not available in many areas. Furthermore, the accuracy problem still arises considering the linear model could not address the non-relationship between reflectance and depth due to bottom variations and noise. Convolutional Neural Networks (CNN) offers the ability to capture the connection between neighbouring pixels and the non-linear relationship. These CNN characteristics make it compelling to be used for shallow water depth extraction. We investigate the accuracy of different architectures using different window sizes and band combinations. We use Sentinel-2 Level 2A images to provide reflectance values, and Lidar and Multi Beam Echo Sounder (MBES) datasets are used as depth references to train and test the model. A set of Sentinel-2 and in-situ depth subimage pairs are extracted to perform CNN training. The model is compared to the linear transform and applied to two other study areas. Resulting accuracy ranges from 1.3 m to 1.94 m, and the coefficient of determination reaches 0.94. The SDB model generated using a window size of 9x9 indicates compatibility with the reference depths, especially at areas deeper than 15 m. The addition of both short wave infrared bands to the four visible bands in training improves the overall accuracy of SDB. The implementation of the pre-trained model to other study areas provides similar results depending on the water conditions.

scholarly journals Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Shazia Akbar ◽  
Mohammad Peikari ◽  
Sherine Salama ◽  
Azadeh Yazdan Panah ◽  
Sharon Nofech-Mozes ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Traditional Approach ◽  
Quantitative Measure ◽  
Image Data ◽  
Qualitative Assessment ◽  
Residual Cancer Burden ◽  
Automated Algorithms ◽  
Tumour Cellularity

Abstract The residual cancer burden index is an important quantitative measure used for assessing treatment response following neoadjuvant therapy for breast cancer. It has shown to be predictive of overall survival and is composed of two key metrics: qualitative assessment of lymph nodes and the percentage of invasive or in situ tumour cellularity (TC) in the tumour bed (TB). Currently, TC is assessed through eye-balling of routine histopathology slides estimating the proportion of tumour cells within the TB. With the advances in production of digitized slides and increasing availability of slide scanners in pathology laboratories, there is potential to measure TC using automated algorithms with greater precision and accuracy. We describe two methods for automated TC scoring: 1) a traditional approach to image analysis development whereby we mimic the pathologists’ workflow, and 2) a recent development in artificial intelligence in which features are learned automatically in deep neural networks using image data alone. We show strong agreements between automated and manual analysis of digital slides. Agreements between our trained deep neural networks and experts in this study (0.82) approach the inter-rater agreements between pathologists (0.89). We also reveal properties that are captured when we apply deep neural network to whole slide images, and discuss the potential of using such visualisations to improve upon TC assessment in the future.

Detection of Carolina Geranium (Geranium carolinianum) Growing in Competition with Strawberry Using Convolutional Neural Networks

Weed Science ◽  
2018 ◽  
Vol 67 (2) ◽  
pp. 239-245 ◽  
Author(s):  
Shaun M. Sharpe ◽  
Arnold W. Schumann ◽  
Nathan S. Boyd
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Future Research ◽  
Study Objective ◽  
Weed Interference ◽  
Detection Technology ◽  
Optimal Approach ◽  
In Situ Detection

AbstractWeed interference during crop establishment is a serious concern for Florida strawberry [Fragaria×ananassa(Weston) Duchesne ex Rozier (pro sp.) [chiloensis×virginiana]] producers. In situ remote detection for precision herbicide application reduces both the risk of crop injury and herbicide inputs. Carolina geranium (Geranium carolinianumL.) is a widespread broadleaf weed within Florida strawberry production with sensitivity to clopyralid, the only available POST broadleaf herbicide.Geranium carolinianumleaf structure is distinct from that of the strawberry plant, which makes it an ideal candidate for pattern recognition in digital images via convolutional neural networks (CNNs). The study objective was to assess the precision of three CNNs in detectingG. carolinianum. Images ofG. carolinianumgrowing in competition with strawberry were gathered at four sites in Hillsborough County, FL. Three CNNs were compared, including object detection–based DetectNet, image classification–based VGGNet, and GoogLeNet. Two DetectNet networks were trained to detect either leaves or canopies ofG. carolinianum. Image classification using GoogLeNet and VGGNet was largely unsuccessful during validation with whole images (Fscore<0.02). CNN training using cropped images increasedG. carolinianumdetection during validation for VGGNet (Fscore=0.77) and GoogLeNet (Fscore=0.62). TheG. carolinianumleaf–trained DetectNet achieved the highestFscore(0.94) for plant detection during validation. Leaf-based detection led to more consistent detection ofG. carolinianumwithin the strawberry canopy and reduced recall-related errors encountered in canopy-based training. The smaller target of leaf-based DetectNet did increase false positives, but such errors can be overcome with additional training images for network desensitization training. DetectNet was the most viable CNN tested for image-based remote sensing ofG. carolinianumin competition with strawberry. Future research will identify the optimal approach for in situ detection and integrate the detection technology with a precision sprayer.

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