AIMC Topic: Semantics

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Efficient Medical Image Segmentation Based on Knowledge Distillation.

IEEE transactions on medical imaging Nov 30, 2021
Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of existing methods has highly relied on huge computational complexity a...
Semantics Humans Neural Networks, Computer Image Processing, Computer-Assisted
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ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation.

IEEE transactions on medical imaging Nov 30, 2021
Accurate and reliable segmentation of colorectal tumors and surrounding colorectal tissues on 3D magnetic resonance images has critical importance in preoperative prediction, staging, and radiotherapy. Previous works simply combine multilevel feature...
Humans Neural Networks, Computer Magnetic Resonance Imaging Semantics Colorectal Neoplasms Image Processing, Computer-Assisted
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GoVec: Gene Ontology Representation Learning Using Weighted Heterogeneous Graph and Meta-Path.

Journal of computational biology : a journal of computational molecular cell biology Nov 29, 2021
Biomedical knowledge graphs are crucial to support data-intensive applications in the life sciences and health care. These graphs can be extended by generating a heterogeneous graph that contains both ontology terms and biomedical entities. However, ...
Computational Biology Semantics Gene Ontology Machine Learning Algorithms Humans
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Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation.

Sensors (Basel, Switzerland) Nov 25, 2021
Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of...
Image Processing, Computer-Assisted Semantics Neural Networks, Computer
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Neural correlates of word representation vectors in natural language processing models: Evidence from representational similarity analysis of event-related brain potentials.

Psychophysiology Nov 24, 2021
Natural language processing models based on machine learning (ML-NLP models) have been developed to solve practical problems, such as interpreting an Internet search query. These models are not intended to reflect human language comprehension mechani...
Evoked Potentials Nerve Net Young Adult Natural Language Processing Electroencephalography Semantics Machine Learning Brain Humans
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Random Fourier Features-Based Deep Learning Improvement with Class Activation Interpretability for Nerve Structure Segmentation.

Sensors (Basel, Switzerland) Nov 20, 2021
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, imag...
Neural Networks, Computer Semantics Ultrasonography Image Processing, Computer-Assisted Deep Learning
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A Hierarchical Feature Extraction Network for Fast Scene Segmentation.

Sensors (Basel, Switzerland) Nov 20, 2021
Semantic segmentation is one of the most active research topics in computer vision with the goal to assign dense semantic labels for all pixels in a given image. In this paper, we introduce HFEN (Hierarchical Feature Extraction Network), a lightweigh...
Neural Networks, Computer Image Processing, Computer-Assisted Semantics
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Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications.

Computational and mathematical methods in medicine Nov 16, 2021
Word embedding models have recently shown some capability to encode hierarchical information that exists in textual data. However, such models do not explicitly encode the hierarchical structure that exists among words. In this work, we propose a met...
Classification Computational Biology Natural Language Processing Knowledge Bases Machine Learning Humans Semantics Databases, Factual
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Spatially Adaptive Feature Refinement for Efficient Inference.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Nov 12, 2021
Spatial redundancy commonly exists in the learned representations of convolutional neural networks (CNNs), leading to unnecessary computation on high-resolution features. In this paper, we propose a novel Spatially Adaptive feature Refinement (SAR) a...
Neural Networks, Computer Algorithms Semantics
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Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.

Nature communications Nov 9, 2021
In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in the macaque inferotemporal (IT) cortex wi...
Humans Deep Learning Neurons Neural Networks, Computer Brain Temporal Lobe Cerebral Cortex Pattern Recognition, Visual Semantics
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