Transfer learning strategy for plastic pollution detection in soil: Calibration transfer from high-throughput HSI system to NIR sensor

Abstract Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.

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BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

Computational Intelligence and Neuroscience ◽

10.1155/2019/1910624 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 4

Author(s):

Yikui Zhai ◽

He Cao ◽

Wenbo Deng ◽

Junying Gan ◽

Vincenzo Piuri ◽

...

Keyword(s):

Transfer Learning ◽

Classification Accuracy ◽

Learning Strategy ◽

State Of The Art ◽

Activation Function ◽

Training Data ◽

Fine Grained ◽

Pattern Recognition Problem ◽

Face Features ◽

Facial Beauty

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

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Micro-nano-sized polytetrafluoroethylene (teflon) particles as a model of plastic pollution detection in living organisms

Environmental Science and Pollution Research ◽

10.1007/s11356-021-16487-6 ◽

2021 ◽

Author(s):

Alexander A. Karpenko ◽

Vyacheslav S. Odintsov ◽

Aleksandra A. Istomina

Keyword(s):

Plastic Pollution ◽

Pollution Detection ◽

Living Organisms

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Robust Finger Vein Recognition Based on Deep CNN with Spatial Attention and Bias Field Correction

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021400054 ◽

2021 ◽

Vol 30 (01) ◽

pp. 2140005

Author(s):

Zhe Huang ◽

Chengan Guo

Keyword(s):

Spatial Attention ◽

Transfer Learning ◽

Learning Strategy ◽

Bias Field ◽

The Public ◽

Finger Vein ◽

Bias Field Correction ◽

Training Samples ◽

Vein Recognition ◽

Finger Vein Recognition

As one of the biometric information based authentication technologies, finger vein recognition has received increasing attention due to its safety and convenience. However, it is still a challenging task to design an efficient and robust finger vein recognition system because of the low quality of the finger vein images, lack of sufficient number of training samples with image-level annotated information and no pixel-level finger vein texture labels in the public available finger vein databases. In this paper, we propose a novel CNN-based finger vein recognition approach with bias field correction, spatial attention mechanism and a multistage transfer learning strategy to cope with the difficulties mentioned above. In the proposed method, the bias field correction module is to remove the unbalanced bias field of the original images by using a two-dimensional polynomial fitting algorithm, the spatial attention module is to enhance the informative vein texture regions while suppressing the other less informative regions, and the multistage transfer learning strategy is to solve the problem caused by insufficient training for CNN-based model due to lack of labeled training samples in the public finger vein databases. Moreover, several measures, including a label smoothing scheme and data augmentation, are exploited to improve the performance of the proposed method. Extensive experiments have been conducted in the work on three public databases, and the results show that the proposed approach outperforms the existing state-of-the-art methods.

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Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

10.26434/chemrxiv.14397140 ◽

2021 ◽

Author(s):

Jeremy Feinstein ◽

ganesh sivaraman ◽

Kurt Picel ◽

Brian Peters ◽

Alvaro Vazquez-Mayagoitia ◽

...

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

High Throughput ◽

High Throughput Screening ◽

Organic Chemical ◽

Learning Method ◽

Toxicity Data ◽

Chemical Structures ◽

Machine Learning Methods ◽

Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

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Learning from heterogeneous data sources: an application in spatial proteomics

10.1101/022152 ◽

2015 ◽

Cited By ~ 1

Author(s):

Lisa M. Breckels ◽

Sean Holden ◽

David Wojnar ◽

Claire M. Mulvey ◽

Andy Christoforou ◽

...

Keyword(s):

Mass Spectrometry ◽

Support Vector Machine ◽

Transfer Learning ◽

High Throughput ◽

Cell Biology ◽

Heterogeneous Data ◽

Data Sources ◽

Support Vector ◽

Proteomics Data ◽

Heterogeneous Data Sources

AbstractSub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.AbbreviationsLOPITLocalisation of Organelle Proteins by Isotope TaggingPCPProtein Correlation ProfilingMLMachine learningTLTransfer learningSVMSupport vector machinePCAPrincipal component analysisGOGene OntologyCCCellular compartmentiTRAQIsobaric tags for relative and absolute quantitationTMTTandem mass tagsMSMass spectrometry

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TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Sensors ◽

10.3390/s20061724 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1724

Author(s):

Zilu Ying ◽

Chen Xuan ◽

Yikui Zhai ◽

Bing Sun ◽

Jingwen Li ◽

...

Keyword(s):

Transfer Learning ◽

Network Architecture ◽

Learning Strategy ◽

Target Recognition ◽

Recognition Performance ◽

Recognition Rate ◽

Speckle Noise ◽

Small Sample ◽

Operating Conditions ◽

Small Samples

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

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Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

10.26434/chemrxiv.14397140.v1 ◽

2021 ◽

Author(s):

Jeremy Feinstein ◽

ganesh sivaraman ◽

Kurt Picel ◽

Brian Peters ◽

Alvaro Vazquez-Mayagoitia ◽

...

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

High Throughput ◽

High Throughput Screening ◽

Organic Chemical ◽

Learning Method ◽

Toxicity Data ◽

Chemical Structures ◽

Machine Learning Methods ◽

Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

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Within and cross species predictions of plant specialized metabolism genes using transfer learning

10.1101/2020.01.13.112102 ◽

2020 ◽

Author(s):

Bethany M. Moore ◽

Peipei Wang ◽

Pengxiang Fan ◽

Aaron Lee ◽

Bryan Leong ◽

...

Keyword(s):

Transfer Learning ◽

Prediction Models ◽

Learning Strategy ◽

Model Species ◽

Specialized Metabolism ◽

Specialized Metabolites ◽

General Metabolism ◽

Model Predictions ◽

Gene Functions ◽

F Measure

AbstractPlant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure=0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure=0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.

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