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MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

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Meteor: Meta-learning connecting earth problems observed from space

Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm

Satellite remote sensing has become a key technology for monitoring Earth and the processes occurring at its surface. It relies on state-of-the-art machine learning models that require large annotated datasets to capture the extreme diversity of the proble ...

2023

Why is the winner the best?

Jian Wang, Gabriel Girard, Ho Ling Li, Adrien Raphaël Depeursinge, Yong Yang, Fan Xia, Xiao Wang, Jing Li, Hui Wang

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...

Los Alamitos2023

Brain microstructural and functional MRI: developments and application to a rat model of Alzheimer's disease

Yujian Diao

Magnetic resonance imaging (MRI) has been a valuable tool in investigating the pathological cascade of Alzheimer's disease (AD) and its progression, which are still open questions. Although some MRI-derived hallmarks in terms of functional connectivity and ...

EPFL2023

SELF-SUPERVISED ISOTROPIC SUPERRESOLUTION FETAL BRAIN MRI

Michaël Unser, Meritxell Bach Cuadra

Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI) traditionally relies on the availability of several orthogonal low-resolution series of 2-dimensional thick slices (volumes). In practice, only a few low-resolution volumes are acqu ...

New York2023

As modern machine learning continues to achieve unprecedented benchmarks, the resource demands to train these advanced models grow drastically. This has led to a paradigm shift towards distributed training. However, the presence of adversariesâwhether ma ...

EPFL2023

, , ,

In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to unseen multi-modal distributions poses even greater difficulties due to the ...

2023

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma

Jiancheng Yang, Li Zhang, Lin Chen, Qiong Li, Ying Zhu

Objectives To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. Methods A total of 542 patients with clini ...

SPRINGER2023

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Thomas Yu

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

EPFL2022

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...

EPFL2022

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Barbara Bruno, Jauwairia Nasir

In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...

2022