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Deep Classification of Epileptic Signals
Electrophysiological observation plays a major role in epilepsy evaluation.
Neural Memory Plasticity for Anomaly Detection
In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling.
Neural Memory Networks for Seizure Type Classification
Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data.
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.
Towards Interpretable Attention Networks for Cervical Cancer Analysis
Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks for the classification of images of multiple cervical cells.
Video-Based Inpatient Fall Risk Assessment: A Case Study
Inpatient falls are a serious safety issue in hospitals and healthcare facilities.
A Survey on Graph-Based Deep Learning for Computational Histopathology
With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology and biopsy image patches.
The CSIRO Crown-of-Thorn Starfish Detection Dataset
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels.
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains
Medical applications have benefited greatly from the rapid advancement in computer vision.
Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study
In this paper we explore the effective use of images from multiple non-visual and privacy-preserving modalities such as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed pose estimation in two settings.
Continuous Human Action Recognition for Human-Machine Interaction: A Review
With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams.
Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging
A modular camera rig arranged in a cylindrical configuration was designed to automatically capture images of the entire skin surface of a subject synchronously from multiple angles.
A Real-time Edge-AI System for Reef Surveys
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels.
Vision-Based Activity Recognition in Children with Autism-Related Behaviors
Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting.
Automated Coronary Arteries Labeling Via Geometric Deep Learning
Automatic labelling of anatomical structures, such as coronary arteries, is critical for diagnosis, yet existing (non-deep learning) methods are limited by a reliance on prior topological knowledge of the expected tree-like structures.
Continual Test-time Domain Adaptation via Dynamic Sample Selection
DSS consists of dynamic thresholding, positive learning, and negative learning processes.
Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis
Wound management poses a significant challenge, particularly for bedridden patients and the elderly.
Deep Learning Approaches for Seizure Video Analysis: A Review
Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting.
Backpropagation-free Network for 3D Test-time Adaptation
Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data.
Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation
We demonstrate significant performance improvements of up to 29. 6% in all tested motion scenarios in cross-dataset testing on MMPD, even in the presence of dynamic and unconstrained subject motion, emphasizing the benefits of disentangling motion through modeling the 3D facial surface for motion robust facial rPPG estimation.
