A fast and accurate explicit kernel map

Francis, Deena P.; Raimond, Kumudha

doi:10.1007/s10489-019-01538-w

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Remarkably, the RFF-FCN and RFF-Unet achieve the best ranking (first and second place) for Dice and IOU, which are often used to test semantic segmentation tasks. Then, our kernel approach allows preserving a trade-off between network complexity and representation learning capability [34,36].…”

Section: Semantic Segmentation Resultsmentioning

confidence: 99%

“…The fundamental work in [34] introduces the random Fourier features (RFF) estimator founded on Bochner's theorem for stationary kernels [35]. The RFF approach tackles the issues of significant computational and storage cost of kernel matrices [36]. In addition, some RFF variants (most of them approximating a Gaussianbased mapping) have been proposed to improve the computational cost and learning performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation

Jimenez-Castaño

Álvarez-Meza

Aguirre-Ospina

et al. 2021

Sensors

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Peripheral nerve blocking (PNB) is a standard procedure to support regional anesthesia. Still, correct localization of the nerve’s structure is needed to avoid adverse effects; thereby, ultrasound images are used as an aid approach. In addition, image-based automatic nerve segmentation from deep learning methods has been proposed to mitigate attenuation and speckle noise ultrasonography issues. Notwithstanding, complex architectures highlight the region of interest lacking suitable data interpretability concerning the learned features from raw instances. Here, a kernel-based deep learning enhancement is introduced for nerve structure segmentation. In a nutshell, a random Fourier features-based approach was utilized to complement three well-known semantic segmentation architectures, e.g., fully convolutional network, U-net, and ResUnet. Moreover, two ultrasound image datasets for PNB were tested. Obtained results show that our kernel-based approach provides a better generalization capability from image segmentation-based assessments on different nerve structures. Further, for data interpretability, a semantic segmentation extension of the GradCam++ for class-activation mapping was used to reveal relevant learned features separating between nerve and background. Thus, our proposal favors both straightforward (shallow) and complex architectures (deeper neural networks).

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Section: Semantic Segmentation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%