FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

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Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

BMC Medical Imaging ◽

10.1186/s12880-021-00551-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Markus J. Ankenbrand ◽

Liliia Shainberg ◽

Michael Hock ◽

David Lohr ◽

Laura M. Schreiber

Keyword(s):

Neural Networks ◽

Sensitivity Analysis ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Cardiac Mri ◽

Magnetic Resonance Images ◽

Model Interpretation ◽

First Case ◽

Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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Adaptive Shell Spherical Reflector Actuated with PVDF-TrFE Thin Film Strain Actuators

Actuators ◽

10.3390/act10010007 ◽

2020 ◽

Vol 10 (1) ◽

pp. 7

Author(s):

Kainan Wang ◽

Thomas Godfroid ◽

Damien Robert ◽

André Preumont

Keyword(s):

Thin Film ◽

Active Material ◽

Quadratic Model ◽

Influence Functions ◽

Regularization Technique ◽

Thin Polymer ◽

Optical Modes ◽

Electrostrictive Material ◽

Ill Conditioning ◽

Good Agreement

This paper discusses the design and manufacturing of a thin polymer spherical adaptive reflector of diameter D=200 mm, controlled by an array of 25 independent electrodes arranged in a keystone configuration actuating a thin film of PVDF-TrFE in d31-mode. The 5 μm layer of electrostrictive material is spray-coated. The results of the present study confirm that the active material can be modelled by a unidirectional quadratic model and that excellent properties can be achieved if the material is properly annealed. The experimental influence functions of the control electrodes are determined by a quasi-static harmonic technique; they are in good agreement with the numerical simulations and their better circular symmetry indicates a clear improvement in the manufacturing process, as compared to a previous study. The low order optical modes can be reconstructed by combining the 25 influence functions; a regularization technique is used to alleviate the ill-conditioning of the Jacobian and allow to approximate the optical modes with reasonable voltages.

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The Difficulty of Predicting Eastern Spruce Dwarf Mistletoe in Lowland Black Spruce: Model Benchmarking in Northern Minnesota, USA

Forests ◽

10.3390/f12070843 ◽

2021 ◽

Vol 12 (7) ◽

pp. 843

Author(s):

Ella R. Gray ◽

Matthew B. Russell ◽

Marcella A. Windmuller-Campione

Keyword(s):

Stand Structure ◽

Black Spruce ◽

Forest Health ◽

Basal Area ◽

Predictive Performance ◽

Stand Age ◽

Management Decisions ◽

Dwarf Mistletoe ◽

Growing Seasons ◽

Model Interpretation

Insects, fungi, and diseases play an important role in forest stand development and subsequently, forest management decisions and treatments. As these disturbance agents commonly occur within and across landscapes, modeling has often been used to inform forest planning and management decisions. However, models are rarely benchmarked, leaving questions about their utility. Here, we assessed the predictive performance of a Bayesian hierarchical model through on–the-ground sampling to explore what features of stand structure or composition may be important factors related to eastern spruce dwarf mistletoe (Arceuthobium pusillum Peck) presence in lowland black spruce (Picea mariana (Mill.) B. S. P.). Twenty-five state-owned stands included in the predictive model were sampled during the 2019 and 2020 growing seasons. Within each stand, data related to the presence of eastern spruce dwarf mistletoe, stand structure, and species composition were collected. The model accurately predicted eastern spruce dwarf mistletoe occurrence for 13 of the 25 stands. The amount of living and dead black spruce basal area differed significantly based on model prediction and observed infestation, but trees per hectare, total living basal area, diameter at breast height, stand age, and species richness were not significantly different. Our results highlight the benefits of model benchmarking to improve model interpretation as well as to inform our understanding of forest health problems across diverse stand conditions.

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