Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Real-time parameter updating for nonlinear digital twins using inverse mapping models and transient-based features

In the context of digital twins, it is essential that a model gives an accurate description of the (controlled) dynamic behavior of a physical system during the system’s entire operational life. Therefore, model updating techniques are required that enable real-time updating of physically interpretable parameter values and are applicable to a wide range of (nonlinear) dynamical systems. As traditional, iterative, parameter updating methods may be computationally too expensive for real-time updating, the inverse mapping parameter updating (IMPU) method is proposed as an alternative. For this method, first, an artificial neural network (ANN) is trained offline using novel features of simulated transient response data. Then, in the online phase, this ANN maps, with little computational cost, a set of measured output response features to parameter estimates enabling real-time model updating. In this paper, various types of transient response features are introduced to update parameter values of nonlinear dynamical systems with increased computational efficiency and accuracy. To analyze the efficacy of these features, the IMPU method is applied to a (simulated) nonlinear multibody system. It is shown that a smart selection of features, based on, e.g., the frequency content of the transient response, can improve the accuracy of the estimated parameter values, leading to more accurate updated models. Furthermore, the generalization capabilities of the ANNs are analyzed for these feature types, by varying the number of training samples and assessing the effect of incomplete training data. It is shown that the IMPU method can predict parameter values that are not part of the training data with acceptable accuracy as well

Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models

In order to ensure that a digital twin accurately describes the dynamic behavior of its corresponding physical system, model updating is typically applied. This chapter introduces a (near) real-time method that uses inverse mapping models to update first-principles-based nonlinear dynamics models. The inverse mapping model infers a set of physically interpretable updating parameter values on the basis of a set of time-domain features extracted from measurements on the real system. Here, the inverse model is given by an artificial neural network that is trained using simulated data. By using a simple nonlinear multibody model, it is illustrated that this method is able to accurately and precisely update parameter values with low computational effort.</p

Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models

In order to ensure that a digital twin accurately describes the dynamic behavior of its corresponding physical system, model updating is typically applied. This chapter introduces a (near) real-time method that uses inverse mapping models to update first-principles-based nonlinear dynamics models. The inverse mapping model infers a set of physically interpretable updating parameter values on the basis of a set of time-domain features extracted from measurements on the real system. Here, the inverse model is given by an artificial neural network that is trained using simulated data. By using a simple nonlinear multibody model, it is illustrated that this method is able to accurately and precisely update parameter values with low computational effort

The competing roles of i-ZnO in Cu(ln,Ga)Se¬2 solar cells

The electrical role of the highly resistive and transparent (HRT) i-ZnO layer in Cu(In, Ga)Se2(CIGS) solar cells is investigated. By tuning the resistivity of atomic layer deposited (ALD) i-ZnO through the use of post-growth O2-plasma treatments, it is shown that low i-ZnO carrier densities (i.e. high resistivities) actually restrict the performance of CIGS solar cells by reducing the extent of band-bending of the CdS/CIGS junction (the effect of series resistance is ruled out as the origin of any observed differences). This is the first evidence that i-ZnO has a negative electrical effect in CIGS solar cells (alongside the positive effect of shunt mitigation), and based on these results, attempts to maximise resistivity of the i-ZnO (typically sought-after for this HRT layer) are not recommended. Device efficiencies of 12.5% were obtained when using low resistivity as-grown ALD i-ZnO (resistivity, ρ=0.6 Ω cm, carrier density, n=3.5·1018 cm−3, and work function, Φ=4.06 eV), but this decreased to 11.5% when using high resistivity, plasma-treated ALD i-ZnO (ρ=134 Ω cm, n=0.2·1018 cm−3, and Φ=4.21 eV). SCAPS modelling revealed the reason for the difference to be the effect that the i-ZnO work function (controlled by carrier density) has on the band-bending and built-in voltage, Vbi, of the main junction. Capacitance-voltage experiments confirmed that the Vbi is lower (∆Vbi~0.1 V) when using low carrier density, high resistivity i-ZnO. This general effect was also found when using RF-sputtered i-ZnO, whereby the inclusion of high resistivity i-ZnO similarly generated lower efficiencies (15.0%) than low resistivity i-ZnO (15.9%)

Boron-Doped Silicon Surfaces from B₂H₆ Passivated by ALD Al₂O₃ for Solar Cells

A p+-doping method for silicon solar cells is presented whereby boron atoms from a pure boron (PureB) layer deposited by chemical vapor deposition using B2H6 as precursor were thermally diffused into silicon. The applicability of this doping process for the doped surfaces of silicon solar cells was evaluated in terms of surface morphology after thermal diffusion, the boron dopant profiles, and sheet resistances, as well as the recombination parameter J0p+, when the doped layers were passivated by Al2O3 films prepared by atomic layer deposition. Adequate surface passivation could be achieved with a surface recombination contribution to J0p+ o

Expanding thermal plasma chemical vapour deposition of ZnO:Al layers for CIGS solar cells

Aluminium-doped zinc oxide (ZnO:Al) grown by expanding thermal plasma chemical vapour deposition (ETP-CVD) has demonstrated excellent electrical and optical properties, which make it an attractive candidate as a transparent conductive oxide for photovoltaic applications. However, when depositing ZnO:Al on CIGS solar cell stacks, one should be aware that high substrate temperature processing (i.e., &gt;200°C) can damage the crucial underlying layers/interfaces (such as CIGS/CdS and CdS/i-ZnO). In this paper, the potential of adopting ETP-CVD ZnO:Al in CIGS solar cells is assessed: the effect of substrate temperature during film deposition on both the electrical properties of the ZnO:Al and the eventual performance of the CIGS solar cells was investigated. For ZnO:Al films grown using the high thermal budget (HTB) condition, lower resistivities, , were achievable (~5 × 10-4¿O·cm) than those grown using the low thermal budget (LTB) conditions (~2 × 10-3¿O·cm), whereas higher CIGS conversion efficiencies were obtained for the LTB condition (up to 10.9%) than for the HTB condition (up to 9.0%). Whereas such temperature-dependence of CIGS device parameters has previously been linked with chemical migration between individual layers, we demonstrate that in this case it is primarily attributed to the prevalence of shunt currents