163 research outputs found
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Computational Approaches to Drug Profiling and Drug-Protein Interactions
Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a
long period of stagnation in drug approvals. Due to the extreme costs associated with
introducing a drug to the market, locating and understanding the reasons for clinical failure
is key to future productivity. As part of this PhD, three main contributions were made in
this respect. First, the web platform, LigNFam enables users to interactively explore
similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly,
two deep-learning-based binding site comparison tools were developed, competing with
the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the
open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold
relationships and has already been used in multiple projects, including integration into a
virtual screening pipeline to increase the tractability of ultra-large screening experiments.
Together, and with existing tools, the contributions made will aid in the understanding of
drug-protein relationships, particularly in the fields of off-target prediction and drug
repurposing, helping to design better drugs faster
Cerebrovascular dysfunction in cerebral small vessel disease
INTRODUCTION:
Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD.
METHODS:
Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs.
RESULTS:
Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion.
DISCUSSION:
Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function
CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images
The visual examination of tissue biopsy sections is fundamental for cancer
diagnosis, with pathologists analyzing sections at multiple magnifications to
discern tumor cells and their subtypes. However, existing attention-based
multiple instance learning (MIL) models, used for analyzing Whole Slide Images
(WSIs) in cancer diagnostics, often overlook the contextual information of
tumor and neighboring tiles, leading to misclassifications. To address this, we
propose the Context-Aware Multiple Instance Learning (CAMIL) architecture.
CAMIL incorporates neighbor-constrained attention to consider dependencies
among tiles within a WSI and integrates contextual constraints as prior
knowledge into the MIL model. We evaluated CAMIL on subtyping non-small cell
lung cancer (TCGA-NSCLC) and detecting lymph node (CAMELYON16) metastasis,
achieving test AUCs of 0.959\% and 0.975\%, respectively, outperforming other
state-of-the-art methods. Additionally, CAMIL enhances model interpretability
by identifying regions of high diagnostic value.Comment: 16 pages, 4 figure
Foundational Models in Medical Imaging: A Comprehensive Survey and Future Vision
Foundation models, large-scale, pre-trained deep-learning models adapted to a
wide range of downstream tasks have gained significant interest lately in
various deep-learning problems undergoing a paradigm shift with the rise of
these models. Trained on large-scale dataset to bridge the gap between
different modalities, foundation models facilitate contextual reasoning,
generalization, and prompt capabilities at test time. The predictions of these
models can be adjusted for new tasks by augmenting the model input with
task-specific hints called prompts without requiring extensive labeled data and
retraining. Capitalizing on the advances in computer vision, medical imaging
has also marked a growing interest in these models. To assist researchers in
navigating this direction, this survey intends to provide a comprehensive
overview of foundation models in the domain of medical imaging. Specifically,
we initiate our exploration by providing an exposition of the fundamental
concepts forming the basis of foundation models. Subsequently, we offer a
methodical taxonomy of foundation models within the medical domain, proposing a
classification system primarily structured around training strategies, while
also incorporating additional facets such as application domains, imaging
modalities, specific organs of interest, and the algorithms integral to these
models. Furthermore, we emphasize the practical use case of some selected
approaches and then discuss the opportunities, applications, and future
directions of these large-scale pre-trained models, for analyzing medical
images. In the same vein, we address the prevailing challenges and research
pathways associated with foundational models in medical imaging. These
encompass the areas of interpretability, data management, computational
requirements, and the nuanced issue of contextual comprehension.Comment: The paper is currently in the process of being prepared for
submission to MI
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning
Aortic stenosis (AS) is a degenerative valve condition that causes
substantial morbidity and mortality. This condition is under-diagnosed and
under-treated. In clinical practice, AS is diagnosed with expert review of
transthoracic echocardiography, which produces dozens of ultrasound images of
the heart. Only some of these views show the aortic valve. To automate
screening for AS, deep networks must learn to mimic a human expert's ability to
identify views of the aortic valve then aggregate across these relevant images
to produce a study-level diagnosis. We find previous approaches to AS detection
yield insufficient accuracy due to relying on inflexible averages across
images. We further find that off-the-shelf attention-based multiple instance
learning (MIL) performs poorly. We contribute a new end-to-end MIL approach
with two key methodological innovations. First, a supervised attention
technique guides the learned attention mechanism to favor relevant views.
Second, a novel self-supervised pretraining strategy applies contrastive
learning on the representation of the whole study instead of individual images
as commonly done in prior literature. Experiments on an open-access dataset and
an external validation set show that our approach yields higher accuracy while
reducing model size.Comment: multiple-instance learning; self-supervised learning; semi-supervised
learning; medical imagin
REPRESENTATION LEARNING WITH ADDITIONAL STRUCTURES
The ability to learn meaningful representations of complex, high-dimensional data like image and text for various downstream tasks has been the cornerstone of the modern deep learning success story. Most approaches that succeed in meaningful representation learning of the input data rely on prior knowledge of the underlying data structure to inject appropriate inductive biases into their frameworks. Prime examples of which range from the convolutional neural network (CNN) for images, to the recurrent neural network (RNN) for sequences, and to the recent trend of attention-based models (e.g. transformers) for incorporating relational information. However, most of the traditional approaches focus on a learning setup where there is a single input (and a single output if in a supervised setting). With the rapidly growing varieties of data being collected and the increasing complexity of the structures that underlie them, approaches that are able to take advantage of the additional data structures for better representation learning are needed. To this end, we introduce frameworks to learn better representations of complex data with additional structures in four arenas, where we gradually shift from supervised learning, to ``pseudo-supervised'' learning, and lastly to unsupervised learning. More specifically, we first propose a supervised approach that exploits relational-information among set elements for learning representations of set-structured data. We then propose a clustering approach that utilizes side-information, i.e. information that is related to the final clustering goal but not directly indicative of the clustering results (hence ``pseudo-supervised'' learning), for learning representations that are better for clustering. Next we introduce another clustering approach that leverages the structural assumption that data samples in each cluster form a trajectory. Lastly, we propose a general representation learning framework for learning interpretable representations of multimodal data.Doctor of Philosoph
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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