Human linear template with mammographic backgrounds estimated with a genetic algorithm

Castella, Cyril; Abbey, Craig K.; Eckstein, Miguel P.; Verdun, Francis R.; Kinkel, Karen; Bochud, François

doi:10.1364/josaa.24.0000b1

Cited by 15 publications

(43 citation statements)

References 39 publications

(66 reference statements)

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“…Although it is impossible to consider all nonlinear models, here we evaluated a model that uses a linear combination of Gabor channels with nonlinear responses (Foley, 1994;Watson & Solomon, 1997;Zhang et al, 2006) to assess the impact of channel nonlinearities on the estimated linear behavioral receptive field. Because the model is nonlinear, estimating its linear component also requires an iterative technique (e.g., Ringach et al, 2002;Castella et al, 2007). 10 for a schematic of the model).…”

Section: Estimation Of Linear Component Of Nonlinear Models For Naturmentioning

confidence: 99%

“…We measured the effect of including an excitatory channel response nonlinearity and also a divisive inhibitory nonlinearity in which the activity of a channel is reduced by the activity of channels with similar spatial frequency and orientation (Foley, 1994;Watson & Solomon, 1997;Zhang et al, 2006;see Fig. Appendix A describes details of estimating the linear template for the nonlinear model (Castella et al, 2007). We evolved the linear weights combining the channel responses to maximize accuracy detecting the Gaussian signal in natural images.…”

Section: Estimation Of Linear Component Of Nonlinear Models For Naturmentioning

confidence: 99%

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Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

2009

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The neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center-surround organization of receptive fields in early vision.

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Section: Estimation Of Linear Component Of Nonlinear Models For Naturmentioning

confidence: 99%

Section: Estimation Of Linear Component Of Nonlinear Models For Naturmentioning

confidence: 99%

Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

2009

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“…Note that Eq (20) can be modified by substituting I j with S j so that the model fits the human confidence rating. A somewhat similar consideration is presented in (Abbey and Eckstein, 2002, Castella et al, 2007). …”

Section: Methodsmentioning

confidence: 74%

“…For a comparison between image-domain spatial templates such as those in Eq 10, and human observers’ image-domain spatial templates, see (Abbey and Eckstein, 2002, Castella et al, 2007). …”

Section: Methodsmentioning

confidence: 99%

Evaluation of the channelized Hotelling observer with an internal-noise model in a train-test paradigm for cardiac SPECT defect detection

Brankov¹

2013

Phys. Med. Biol.

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The channelized Hotelling observer (CHO) has become a widely used approach for evaluating medical image quality, acting as a surrogate for human observers in early-stage research on assessment and optimization of imaging devices and algorithms. The CHO is typically used to measure lesion detectability. Its popularity stems from experiments showing that the CHO’s detection performance can correlate well with that of human observers. In some cases, CHO performance overestimates human performance; to counteract this effect, an internal-noise model is introduced, which allows the CHO to be tuned to match human observer performance. Typically, this tuning is achieved using example data obtained from human observers. We argue that this internal-noise tuning step is essentially a model training exercise; therefore, just as in supervised learning, it is essential to test the CHO with an internal-noise model on a set of data that is distinct from that used to tune (train) the model. Furthermore, we argue that, if the CHO is to provide useful insights about new imaging algorithms or devices, the test data should reflect such potential differences from the training data; it is not sufficient simply to use new noise realizations of the same imaging method. Motivated by these considerations, the novelty of this paper is the use of new model selection criteria to evaluate ten established internal-noise models, utilizing four different channel models, in a train-test approach. Though not the focus of the paper, a new internal-noise model is also proposed that outperformed the ten established models in the cases tested. The results, using cardiac perfusion SPECT data, show that the proposed train-test approach is necessary, as judged by the newly proposed model selection criteria, to avoid spurious conclusions. The results also demonstrate that, in some models, the optimal internal-noise parameter is very sensitive to the choice of training data; therefore, these models are prone to overfitting, and will not likely generalize well to new data. In addition, we present an alternative interpretation of the CHO as a penalized linear regression wherein the penalization term is defined by the internal noise model.

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“…Different types of internal noise models have also been proposed (see [11]-[13] for a review). Alternative numerical observers are, for example, proposed in [14]-[17] based on human linear template estimation; in [18] and [19] efforts are made to incorporate nonlinearity into models.…”

Section: Introductionmentioning

confidence: 99%

Generalization Evaluation of Machine Learning Numerical Observers for Image Quality Assessment

Kalayeh

Marin

Brankov

2013

IEEE Trans. Nucl. Sci.

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In this paper, we present two new numerical observers (NO) based on machine learning for image quality assessment. The proposed NOs aim to predict human observer performance in a cardiac perfusion-defect detection task for single-photon emission computed tomography (SPECT) images. Human observer (HumO) studies are now considered to be the gold standard for task-based evaluation of medical images. However such studies are impractical for use in early stages of development for imaging devices and algorithms, because they require extensive involvement of trained human observers who must evaluate a large number of images. To address this problem, numerical observers (also called model observers) have been developed as a surrogate for human observers. The channelized Hotelling observer (CHO), with or without internal noise model, is currently the most widely used NO of this kind. In our previous work we argued that development of a NO model to predict human observers' performance can be viewed as a machine learning (or system identification) problem. This consideration led us to develop a channelized support vector machine (CSVM) observer, a kernel-based regression model that greatly outperformed the popular and widely used CHO. This was especially evident when the numerical observers were evaluated in terms of generalization performance. To evaluate generalization we used a typical situation for the practical use of a numerical observer: after optimizing the NO (which for a CHO might consist of adjusting the internal noise model) based upon a broad set of reconstructed images, we tested it on a broad (but different) set of images obtained by a different reconstruction method. In this manuscript we aim to evaluate two new regression models that achieve accuracy higher than the CHO and comparable to our earlier CSVM method, while dramatically reducing model complexity and computation time. The new models are defined in a Bayesian machine-learning framework: a channelized relevance vector machine (CRVM) and a multi-kernel CRVM (MKCRVM).

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Human linear template with mammographic backgrounds estimated with a genetic algorithm

Cited by 15 publications

References 39 publications

Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

Evaluation of the channelized Hotelling observer with an internal-noise model in a train-test paradigm for cardiac SPECT defect detection

Generalization Evaluation of Machine Learning Numerical Observers for Image Quality Assessment

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