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

https://doi.org/10.3390/s21227741
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Automatic Peripheral Nerve Segmentation in Presence of Multiple Annotators
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DeepNerve: A New Convolutional Neural Network for the Localization and Segmentation of the Median Nerve in Ultrasound Image Sequences
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Peripheral nerve segmentation using Nonparametric Bayesian Hierarchical Clustering
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A probabilistic framework based on SLIC-superpixel and Gaussian processes for segmenting nerves in ultrasound images
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Random features for Kernel Deep Convex Network
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Automatic nerve segmentation of ultrasound images
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Deep Learning for Semantic Segmentation of Brachial Plexus Nervesin Ultrasound Images Using U-Net and M-Net
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Ultrasound Nerve Segmentation of Brachial Plexus Based on Optimized ResU-Net
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Road Extraction by Deep Residual U-Net
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Automatic Localization of the Needle Target for Ultrasound-Guided Epidural Injections
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Learning High-Level Feature by Deep Belief Networks for 3-D Model Retrieval and Recognition
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Fully Convolutional Networks for Semantic Segmentation
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Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
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Improving needle tip identification during ultrasound‐guided procedures in anaesthetic practice
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
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Automated and real-time segmentation of suspicious breast masses using convolutional neural network
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Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract)
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Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis
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Medical Image Segmentation based on U-Net: A Review
inferredcites →https://doi.org/10.3390/app11125409openalex-snapshot
Regularized Chained Deep Neural Network Classifier for Multiple Annotators
inferredcites →https://doi.org/10.5614/itbj.ict.res.appl.2019.13.3.5openalex-snapshot
Ultrasound Nerve Segmentation Using Deep Probabilistic Programming
inferredcites →https://doi.org/10.7551/mitpress/4175.001.0001openalex-snapshot
Learning with Kernels

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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EFE-HCTNet: An edge feature enhanced hybrid CNN-transformer network for the automated identification of nerves in ultrasound images
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LFNS-Net: a lightweight partial convolution and attention network for femoral nerve segmentation in ultrasound images
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Opportunities of AI-powered applications in anesthesiology to enhance patient safety
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Brachial Plexus Nerve Trunk Recognition From Ultrasound Images: A Comparative Study of Deep Learning Models
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3D probe localization from 2D ultrasound images using an RFF-enhanced deep neural network
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Feet Segmentation for Regional Analgesia Monitoring Using Convolutional RFF and Layer-Wise Weighted CAM Interpretability
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Explainable Deep Kernel Learning for Interpretable Automatic Modulation Classification
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Deep Learning on Ultrasound Images Visualizes the Femoral Nerve with Good Precision
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Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images
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Ultrasonic Imaging and Sensors
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Investigation of Appropriate Scaling of Networks and Images for Convolutional Neural Network-Based Nerve Detection in Ultrasound-Guided Nerve Blocks
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