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Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

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Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Rahul Sharma

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system ...

MDPI2023

Point-process-based Representation Learning for Electronic Health Records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions o ...

IEEE2023

Track: Machine Learning in Protein Science De Novo Design of Viral Epitope Mimetics with Non-Regular Structural Features

Bruno Emanuel Ferreira De Sousa Correia, Karla Montserrat Castro Gilabert, Anastassia Andreevna Vorobieva

WILEY2023

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Martin Weigert

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a communitywide challenge using the largest availab ...

Elsevier2023

Latent Representation of CFD Meshes and Application to Airfoil Aerodynamics

Pascal Fua, Benoît Alain René Guillard, Zhen Wei

Mesh manipulation is central to Computational Fluid Dynamics (CFD). However, creating appropriate computational meshes often involves substantial manual intervention that has to be repeated each time the target shape changes. To address this problem, we p ...

American Institute of Aeronautics and Astronautics2023

Hessian-based toolbox for reliable and interpretable machine learning in physics

Patrick Hümbeli

Machine learning (ML) techniques applied to quantum many-body physics have emerged as a new research field. While the numerical power of this approach is undeniable, the most expressive ML algorithms, such as neural networks, are black boxes: The user does ...

2022

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI

Jean-Philippe Thiran, Francesco La Rosa

Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesio ...

WILEY2022

Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension

Florent Gérard Krzakala, Bruno Loureiro

From the sampling of data to the initialisation of parameters, randomness is ubiquitous in modern Machine Learning practice. Understanding the statistical fluctuations engendered by the different sources of randomness in prediction is therefore key to unde ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022