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Found 16 papers, 4 papers with code
A study on the adequacy of common IQA measures for medical images
Image quality assessment (IQA) is standard practice in the development stage of novel machine learning algorithms that operate on images.
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization
This study contributes to PFL by establishing a solid theoretical foundation for the proposed method and offering a robust, ready-to-use framework that effectively addresses the challenges posed by non-IID data in FL.
A study of why we need to reassess full reference image quality assessment with medical images
Image quality assessment (IQA) is not just indispensable in clinical practice to ensure high standards, but also in the development stage of novel algorithms that operate on medical images with reference data.
The curious case of the test set AUROC
Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace.
Recent Methodological Advances in Federated Learning for Healthcare
We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare.
REFORMS: Reporting Standards for Machine Learning Based Science
Machine learning (ML) methods are proliferating in scientific research.
Reinterpreting survival analysis in the universal approximator age
We aim to provide the tools needed to fully harness the potential of survival analysis in deep learning.
Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data
Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites.
Estimating defection in subscription-type markets: empirical analysis from the scholarly publishing industry
We show that this approach can be both accurate as well as uniquely useful in the business-to-business context, with which the scholarly publishing business model shares similarities.
Navigating the challenges in creating complex data systems: a development philosophy
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.
Classification of datasets with imputed missing values: does imputation quality matter?
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness.
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.
Sequential hypothesis testing in machine learning, and crude oil price jump size detection
In this paper we present a sequential hypothesis test for the detection of general jump size distrubution.
Chan-Vese Reformulation for Selective Image Segmentation
Selective segmentation involves incorporating user input to partition an image into foreground and background, by discriminating between objects of a similar type.
