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Semantics

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Visualization of medical concepts represented using word embeddings: a scoping review.

BMC medical informatics and decision making
BACKGROUND: Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language p...

Skeleton-Based Spatio-Temporal U-Network for 3D Human Pose Estimation in Video.

Sensors (Basel, Switzerland)
Despite the great progress in 3D pose estimation from videos, there is still a lack of effective means to extract spatio-temporal features of different granularity from complex dynamic skeleton sequences. To tackle this problem, we propose a novel, s...

A morphometric analysis of the osteocyte canaliculus using applied automatic semantic segmentation by machine learning.

Journal of bone and mineral metabolism
INTRODUCTION: Osteocytes play a role as mechanosensory cells by sensing flow-induced mechanical stimuli applied on their cell processes. High-resolution imaging of osteocyte processes and the canalicular wall are necessary for the analysis of this me...

Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos.

Medical image analysis
Out of all existing frameworks for surgical workflow analysis in endoscopic videos, action triplet recognition stands out as the only one aiming to provide truly fine-grained and comprehensive information on surgical activities. This information, pre...

Self-supervised Natural Image Reconstruction and Large-scale Semantic Classification from Brain Activity.

NeuroImage
Reconstructing natural images and decoding their semantic category from fMRI brain recordings is challenging. Acquiring sufficient pairs of images and their corresponding fMRI responses, which span the huge space of natural images, is prohibitive. We...

TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
The explanation for deep neural networks has drawn extensive attention in the deep learning community over the past few years. In this work, we study the visual saliency, a.k.a. visual explanation, to interpret convolutional neural networks. Compared...

Object-Based Reliable Visual Navigation for Mobile Robot.

Sensors (Basel, Switzerland)
Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training...

Graph-Powered Interpretable Machine Learning Models for Abnormality Detection in Ego-Things Network.

Sensors (Basel, Switzerland)
In recent days, it is becoming essential to ensure that the outcomes of signal processing methods based on machine learning (ML) data-driven models can provide interpretable predictions. The interpretability of ML models can be defined as the capabil...

Bias-Eliminated Semantic Refinement for Any-Shot Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
When training samples are scarce, the semantic embedding technique, i. e., describing class labels with attributes, provides a condition to generate visual features for unseen objects by transferring the knowledge from seen objects. However, semantic...

Semantic Segmentation Dataset for AI-Based Quantification of Clean Mucosa in Capsule Endoscopy.

Medicina (Kaunas, Lithuania)
: Capsule endoscopy (CE) for bowel cleanliness evaluation primarily depends on subjective methods. To objectively evaluate bowel cleanliness, we focused on artificial intelligence (AI)-based assessments. We aimed to generate a large segmentation data...