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Found 13 papers, 2 papers with code
Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images
Hypoxia occurs when tumour cells outgrow their blood supply, leading to regions of low oxygen levels within the tumour.
Low-field magnetic resonance image enhancement via stochastic image quality transfer
Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field.
Representational Ethical Model Calibration
Equity is widely held to be fundamental to the ethics of healthcare.
Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learning
While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities might not be available, the task remains time-consuming and prone to human inconsistencies.
Image Processing Tools for Financial Time Series Classification
The application of deep learning to time series forecasting is one of the major challenges in present machine learning.
Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients
1. 5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robustness to power failures.
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
In this paper we propose a probabilistic decimation simulator to improve robustness of model training.
Whole-Sample Mapping of Cancerous and Benign Tissue Properties
Structural and mechanical differences between cancerous and healthy tissue give rise to variations in macroscopic properties such as visual appearance and elastic modulus that show promise as signatures for early cancer detection.
Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data
Our Locality-specific Elastic-Net based Malaria Prediction System (LEMPS) achieves good generalization performance, both in magnitude and direction of the prediction, when tasked to predict monthly prevalence on previously unseen validation data (MAE<=6x10-2, MSE<=7x10-3) within a range of (+0. 1 to -0. 05) error-tolerance which is relevant and usable for aiding decision-support in a holoendemic setting.
Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy
Here we address the problem of Extended Depth-of-Field (EDoF) in thick blood film microscopy for rapid automated malaria diagnosis.
A Tutorial on Canonical Correlation Methods
Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables.
Principled Non-Linear Feature Selection
Recent non-linear feature selection approaches employing greedy optimisation of Centred Kernel Target Alignment(KTA) exhibit strong results in terms of generalisation accuracy and sparsity.
Learning Non-Linear Feature Maps
Feature selection plays a pivotal role in learning, particularly in areas were parsimonious features can provide insight into the underlying process, such as biology.
