We systematically present these methods and indicate how the adoption of deep learning for the analysis of video recordings of seizures could be approached.
no code implementations • 27 Nov 2023 • Léo Lebrat, Rodrigo Santa Cruz, Remi Chierchia, Yulia Arzhaeva, Mohammad Ali Armin, Joshua Goldsmith, Jeremy Oorloff, Prithvi Reddy, Chuong Nguyen, Lars Petersson, Michelle Barakat-Johnson, Georgina Luscombe, Clinton Fookes, Olivier Salvado, David Ahmedt-Aristizabal
Wound management poses a significant challenge, particularly for bedridden patients and the elderly.
Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications.
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.
Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting.
We present You Only Cut Once (YOCO) for performing data augmentations.
Medical applications have benefited greatly from the rapid advancement in computer vision.
no code implementations • 29 Nov 2021 • Jiajun Liu, Brano Kusy, Ross Marchant, Brendan Do, Torsten Merz, Joey Crosswell, Andy Steven, Nic Heaney, Karl Von Richter, Lachlan Tychsen-Smith, David Ahmedt-Aristizabal, Mohammad Ali Armin, Geoffrey Carlin, Russ Babcock, Peyman Moghadam, Daniel Smith, Tim Davis, Kemal El Moujahid, Martin Wicke, Megha Malpani
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.
Existing deep learning based unsupervised video object segmentation methods still rely on ground-truth segmentation masks to train.
We show that using a kernelised generalised linear model (kGLM) as an inner problem in a DDN yields a large class of commonly used DEQ architectures with a closed-form expression for the hidden layer parameters in terms of the kernel.
We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown.
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.
There are 2000 reference restored images and 6003 original underwater images in the unpaired training set.
Here, we evaluate various state-of-the-art deep learning models and attention-based frameworks for the classification of images of multiple cervical cells.
Inpatient falls are a serious safety issue in hospitals and healthcare facilities.
It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.
Unsupervised image-to-image translation tasks aim to find a mapping between a source domain X and a target domain Y from unpaired training data.
Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world.
Additionally, to the best of our knowledge, our method is the first specialized method to super-resolve mosaic images, whether it be multi-spectral or Bayer.
This paper introduces a novel method to simultaneously super-resolve and colour-predict images acquired by snapshot mosaic sensors.