Zero-dimensional biomarker-based medical action recognition: towards more explainable AI in healthcare

  Medical action recognition is an increasingly necessary task as healthcare has shifted towards digital and more remote methods of patient monitoring. To remotely assess a patient with computer-aided diagnosis it is first necessary to identify different actions. In tandem with action recognition, this remote monitoring also requires biomarker identification. This allows meaningful features to be extracted from the patient’s actions, however, each action often requires a different set of features to be monitored. Finally, the mode of data collection requires both portability and accuracy to be used in the healthcare industry. To combine each of these, one of the best solutions for condition-related action recognition is using videos in combination with human pose estimation to create spatio-temporal skeleton data for the patients which allows further analysis to be fast and accurate. By first using manual feature extraction on the skeleton sequences, it is possible to utilise machine learning to both classify different actions and identify latent features as the biomarkers that distinguish each of the action classes. Thus, this new method proposes a new zero-dimensional feature extraction method to classify skeleton sequences and extract medically meaningful, explainable, and objective biomarkers that can be used for the diagnosis and monitoring of patients in a remote setting.</p

A hybrid method for haemorrhage segmentation in trauma brain CT

Traumatic brain injuries are important causes of disability and death. Physicians use CT or MRI images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of haemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. In this paper, we present a hybrid method to segment the haemorrhage region in trauma brain CT images. Firstly, the images are partitioned to small segments called superpixels and supervoxels in 2D and 3D spaces, respectively. Then the haemorrhage superpixels/supervoxels are grouped using their average intensity as feature. Finally, a distance regularized level-set is used to accurately delineate the exact boundary of the haemorrhage region. Evaluation is performed using the Jaccard overlap measure of our proposed technique against a modified distance regularized level-set and against the manually segmented ground truth. Our results suggest that performing level-set after superpixel/supervoxel segmentation provides better segmentation than superpixel/supervoxel intensity grouping alone and both these schemes perform better than the modified distance regularized level-set evolution method.</p

A cross-platform approach to the treatment of ambylopia

In this paper, we introduce a diagnosis and treatment for amblyopia performed through a game suitable for children aged between 3 and 7. Our method places emphasis on cooperation between the two eyes to achieve a good binocular outcome to aid the recovery of depth perception. Our approach is not limited to a particular device or platform nor even to a aprticular form of game. Several prototype games have been developed, including 2D games and 3D games. © 2013 IEEE.</p

Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage

This paper presents a spatiotemporal deep learning approach for mouse behavioral classification in the home-cage. Using a series of dual-stream architectures with assorted modifications for optimal performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. The dataset in focus is an annotated, publicly available dataset of a singly-housed mouse. We achieved even better classification accuracy by ensembling the best performing models; an Inception-based network and an attention-based network, both of which utilize this feature sharing attribute. Furthermore, we demonstrate through ablation studies that for all models, the feature sharing architectures consistently outperform the conventional 8 dual-stream having standalone streams. In particular, the inception-based architectures showed higher feature sharing gains with their increase in accuracy anywhere between 6.59% and 15.19%. The best-performing models were also further evaluated on other mouse behavioral datasets.  </p