Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis

Gan, Luyun; Yuen, Brosnan; Lü, Tao

doi:10.3390/make1040061

Cited by 9 publications

(13 citation statements)

References 35 publications

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“…The detection of QS complexes is out of the scope of this paper. The following correct ECG recordings from the MIT-BIH database were used for training and testing: 100,101,102,103,104,105,106,109,112,113,115,116,118,119,121,122,123,201,202,208,209,212,213,214,215,217,219,220,221,222,228,230, 231, 232, and 234. Furthermore, the following correct ECG recordings from the European ST-T database were used for training and testing: e0103, e0104, e0111, e0112, e0113, e0115, e0116, e0118, e0123, e0127, e0136, e0147, e0151, e0154, e0159, e0161, e0166, e0170, e0203, e0204, e0206, e0207, e0208, e0210, e0212, e0303, e0306, e0404, e0406, e0408, e0409, e0410, e0411, e0417, e0418, e0509, e0601, e0606, e0607, e0609, e0610, e0611, e0612, e0613, e0615, e0704, e0818, and e1304.…”

Section: Data Preparationmentioning

confidence: 99%

“…For the MIT-BIH NST, the following recordings are used for training and testing: 100,104,108,113,117,122,201,207,212,217,222,231,101,105,109,114,118,123,208,213,219,223,232,102,106,111,115,119,124,203,209,214,220,228,233,103,107,112,116,121,200,205,210,215,221,230, and 234. The QRS complex inverter is applied to the MIT-BIH as it has many inverted QRS complexes.…”

Section: Mit-bih Nstmentioning

confidence: 99%

“…After extraction, they classified the qualities of the cigarettes. Gan et al [113] used PCA together with a multilayer perceptron (MLP) for classifying multiple gasses in near IR spectra. Furthermore, Gan et al [113] used optimal thresholding to balance the false positive errors and the false negative errors in order to obtain the best performance.…”

Section: Chapter 4 Generating Infrared Gaseous Spectra Using Generatimentioning

confidence: 99%

“…Gan et al [113] used PCA together with a multilayer perceptron (MLP) for classifying multiple gasses in near IR spectra. Furthermore, Gan et al [113] used optimal thresholding to balance the false positive errors and the false negative errors in order to obtain the best performance. Chen et al [114] implemented convolutional neural networks (CNNs) for the classification of hyperspectral satellite images.…”

Section: Chapter 4 Generating Infrared Gaseous Spectra Using Generatimentioning

confidence: 99%

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Applications of Machine Learning

Rudström¹

2020

Algorithms for Intelligent Systems

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In this thesis, many machine learning algorithms were applied to electrocardiogram (ECG), spectral analysis, and Field Programmable Gate Arrays (FPGAs). In ECG, QRS complexes are useful for measuring the heart rate and for the segmentation of ECG signals. QRS complexes were detected using WaveletCNN Autoencoder filters and ConvLSTM detectors. The WaveletCNN Autoencoders filters the ECG signals using the wavelet filters, while the ConvLSTM detects the spatial temporal patterns of the QRS complexes. For the spectral analysis topic, the detection of chemical compounds using spectral analysis is useful for identifying unknown substances. However, spectral analysis algorithms require vast amounts of data. To solve this problem, B-spline neural networks were developed for the generation of infrared and ultraviolet/visible spectras. This allowed for the generation of large training datasets from a few experimental measurements. Graphical Processing Units (GPUs) are good for training and testing neural networks. However, using multiple GPUs together is hard because PCIe bus is not suited for scattering operations and reduce operations. FPGAs are more flexible as they can be arranged in a mesh or toroid or hypercube configuration on the PCB. These configurations provide higher data throughput and results in faster computations. A general neural network framework was written in VHDL for Xilinx FPGAs. It allows for any neural network to be trained or tested on FPGAs.

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Section: Data Preparationmentioning

confidence: 99%

Section: Mit-bih Nstmentioning

confidence: 99%

Section: Chapter 4 Generating Infrared Gaseous Spectra Using Generatimentioning

confidence: 99%

Section: Chapter 4 Generating Infrared Gaseous Spectra Using Generatimentioning

confidence: 99%

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Applications of Machine Learning

Rudström¹

2020

Algorithms for Intelligent Systems

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“…Section “ Construction of UAF ” describes the properties of UAF and its training procedure. Section “ Experiments ” shows the UAF’s performances on the CIFAR-10 32 classification, infrared spectra database for 9 gas quantification 33 , BipedalWalker-v2 34 RL, Planetoid/CORA publication classification dataset 35 , and ZINC molecular solubility quantification dataset 36 . Furthermore, a conclusion is presented in Section “ Conclusion and future work ”.…”

Section: Introductionmentioning

confidence: 99%

Universal activation function for machine learning

Yuen

Hoang

Dong

et al. 2021

Sci Rep

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This article proposes a universal activation function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the gradient descent algorithms are able to evolve the UAF to a suitable activation function by tuning the UAF’s parameters. For the CIFAR-10 classification using the VGG-8 neural network, the UAF converges to the Mish like activation function, which has near optimal performance $$F_{1}=0.902\pm 0.004$$ F 1 = 0.902 ± 0.004 when compared to other activation functions. In the graph convolutional neural network on the CORA dataset, the UAF evolves to the identity function and obtains $$F_1=0.835\pm 0.008$$ F 1 = 0.835 ± 0.008 . For the quantification of simulated 9-gas mixtures in 30 dB signal-to-noise ratio (SNR) environments, the UAF converges to the identity function, which has near optimal root mean square error of $$0.489\pm 0.003~\mu {\mathrm{M}}$$ 0.489 ± 0.003 μ M . In the ZINC molecular solubility quantification using graph neural networks, the UAF morphs to a LeakyReLU/Sigmoid hybrid and achieves RMSE=$$0.47\pm 0.04$$ 0.47 ± 0.04 . For the BipedalWalker-v2 RL dataset, the UAF achieves the 250 reward in $${961\pm 193}$$ 961 ± 193 epochs with a brand new activation function, which gives the fastest convergence rate among the activation functions.

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Transformer-based structuring of free-text radiology report databases

et al. 2023

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Objectives To provide insights for on-site development of transformer-based structuring of free-text report databases by investigating different labeling and pre-training strategies. Methods A total of 93,368 German chest X-ray reports from 20,912 intensive care unit (ICU) patients were included. Two labeling strategies were investigated to tag six findings of the attending radiologist. First, a system based on human-defined rules was applied for annotation of all reports (termed “silver labels”). Second, 18,000 reports were manually annotated in 197 h (termed “gold labels”) of which 10% were used for testing. An on-site pre-trained model (Tmlm) using masked-language modeling (MLM) was compared to a public, medically pre-trained model (Tmed). Both models were fine-tuned on silver labels only, gold labels only, and first with silver and then gold labels (hybrid training) for text classification, using varying numbers (N: 500, 1000, 2000, 3500, 7000, 14,580) of gold labels. Macro-averaged F1-scores (MAF1) in percent were calculated with 95% confidence intervals (CI). Results Tmlm,gold (95.5 [94.5–96.3]) showed significantly higher MAF1 than Tmed,silver (75.0 [73.4–76.5]) and Tmlm,silver (75.2 [73.6–76.7]), but not significantly higher MAF1 than Tmed,gold (94.7 [93.6–95.6]), Tmed,hybrid (94.9 [93.9–95.8]), and Tmlm,hybrid (95.2 [94.3–96.0]). When using 7000 or less gold-labeled reports, Tmlm,gold (N: 7000, 94.7 [93.5–95.7]) showed significantly higher MAF1 than Tmed,gold (N: 7000, 91.5 [90.0–92.8]). With at least 2000 gold-labeled reports, utilizing silver labels did not lead to significant improvement of Tmlm,hybrid (N: 2000, 91.8 [90.4–93.2]) over Tmlm,gold (N: 2000, 91.4 [89.9–92.8]). Conclusions Custom pre-training of transformers and fine-tuning on manual annotations promises to be an efficient strategy to unlock report databases for data-driven medicine. Key Points • On-site development of natural language processing methods that retrospectively unlock free-text databases of radiology clinics for data-driven medicine is of great interest. • For clinics seeking to develop methods on-site for retrospective structuring of a report database of a certain department, it remains unclear which of previously proposed strategies for labeling reports and pre-training models is the most appropriate in context of, e.g., available annotator time. • Using a custom pre-trained transformer model, along with a little annotation effort, promises to be an efficient way to retrospectively structure radiological databases, even if not millions of reports are available for pre-training.

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Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis

Cited by 9 publications

References 35 publications

Applications of Machine Learning

Applications of Machine Learning

Universal activation function for machine learning

Transformer-based structuring of free-text radiology report databases

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