100 citations · 161 across the 14 of their papers we have counts for
15 papers
MORPHFED: Federated Learning for Cross-institutional Blood Morphology Analysis
Gabriel Ansah, Eden Ruffell, Delmiro Fernandez-Reyes +1
Automated blood morphology analysis can support hematological diagnostics in low- and middle-income countries (LMICs) but remains sensitive to dataset shifts from staining variabil…
Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillation
Jeremiah Fadugba, Petru Manescu, Bolanle Oladejo +2
Uncertainty estimation is critical for reliable medical image segmentation, particularly in retinal vessel analysis, where accurate predictions are essential for diagnostic applica…
Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images
Petru Manescu, Joseph Geradts, Delmiro Fernandez-Reyes
Hypoxia occurs when tumour cells outgrow their blood supply, leading to regions of low oxygen levels within the tumour. Calculating hypoxia levels can be an important step in under…
Low-field magnetic resonance image enhancement via stochastic image quality transfer
Hongxiang Lin, Matteo Figini, Felice D'Arco +10
Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher…
Representational Ethical Model Calibration
Robert Carruthers, Isabel Straw, James K Ruffle +6
Equity is widely held to be fundamental to the ethics of healthcare. In the context of clinical decision-making, it rests on the comparative fidelity of the intelligence -- evidenc…
Automated Detection of Acute Promyelocytic Leukemia in Blood Films and Bone Marrow Aspirates with Annotation-free Deep Learning
Petru Manescu, Priya Narayanan, Christopher Bendkowski +7
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-reso…
Image Processing Tools for Financial Time Series Classification
Bairui Du, Delmiro Fernandez-Reyes, Paolo Barucca
The application of deep learning to time series forecasting is one of the major challenges in present machine learning. We propose a novel methodology that combines machine learnin…
Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients
Matteo Figini, Hongxiang Lin, Godwin Ogbole +10
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 robu…
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator
Hongxiang Lin, Matteo Figini, Ryutaro Tanno +10
MR images scanned at low magnetic field (T) have lower resolution in the slice direction and lower contrast, due to a relatively small signal-to-noise ratio (SNR) than those fr…
Whole-Sample Mapping of Cancerous and Benign Tissue Properties
Lydia Neary-Zajiczek, Clara Essmann, Neil Clancy +8
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…
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
Biobele J. Brown, Alexander A. Przybylski, Petru Manescu +20
Plasmodium falciparum malaria still poses one of the greatest threats to human life with over 200 million cases globally leading to half-million deaths annually. Of these, 90% of c…
Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy
Petru Manescu, Lydia Neary- Zajiczek, Michael J. Shaw +10
Fast accurate diagnosis of malaria is still a global health challenge for which automated digital-pathology approaches could provide scalable solutions amenable to be deployed in l…
A Tutorial on Canonical Correlation Methods
Viivi Uurtio, João M. Monteiro, Jaz Kandola +3
Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis h…
Principled Non-Linear Feature Selection
Dimitrios Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes
Recent non-linear feature selection approaches employing greedy optimisation of Centred Kernel Target Alignment(KTA) exhibit strong results in terms of generalisation accuracy and…
Learning Non-Linear Feature Maps
Dimitrios Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes
Feature selection plays a pivotal role in learning, particularly in areas were parsimonious features can provide insight into the underlying process, such as biology. Recent approa…