AIMC Topic: Semantics

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Real-Time Semantic Segmentation with Dual Encoder and Self-Attention Mechanism for Autonomous Driving.

Sensors (Basel, Switzerland)
As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply th...

Learning Domain-Agnostic Visual Representation for Computational Pathology Using Medically-Irrelevant Style Transfer Augmentation.

IEEE transactions on medical imaging
Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolv...

Efficient Medical Image Segmentation Based on Knowledge Distillation.

IEEE transactions on medical imaging
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...

ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation.

IEEE transactions on medical imaging
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...

GoVec: Gene Ontology Representation Learning Using Weighted Heterogeneous Graph and Meta-Path.

Journal of computational biology : a journal of computational molecular cell biology
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, ...

Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation.

Sensors (Basel, Switzerland)
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...

Neural correlates of word representation vectors in natural language processing models: Evidence from representational similarity analysis of event-related brain potentials.

Psychophysiology
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...

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

Sensors (Basel, Switzerland)
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...

A Hierarchical Feature Extraction Network for Fast Scene Segmentation.

Sensors (Basel, Switzerland)
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...

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
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...