A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT

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Publications associées (31)

Krab-containing zinc finger protein and cancer

Didier Trono, Priscilla Turelli

The present invention provides methods for detecting cancer as well as agents and compositions for treating cancer by modulating the expression and/or activity of one or more i) KRAB-containing zinc finger protein (KZFP), ii) mRNA encoding a KZFP, and/or i ...

2022

Multiscale light-sheet organoid imaging framework

Raphaël Ortiz, Petr Strnad, Andrea Boni

Live imaging of organoid growth remains a challenge: it requires long-term imaging of several samples simultaneously and dedicated analysis pipelines. Here the authors report an experimental and image processing framework to turn long-term light-sheet imag ...

Nature Portfolio2022

Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation

Tobias Kober, Tom Hilbert, Qian Wang, Yubo Zhao, Yao Zhang

Objective To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with ...

SPRINGER2022

Novel trehalose-based excipients for stabilizing nebulized anti-SARS-CoV-2 antibody

Sandrine Gerber, François Rémi Pierre Noverraz, Solène Marcelle Françoise Marie Passemard

COVID-19 is caused by the infection of the lungs by SARS-CoV-2. Monoclonal antibodies, such as sotrovimab, showed great efficiency in neutralizing the virus before its internalization by lung epithelial cells. However, parenteral routes are still the prefe ...

2023

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

Humoral Responses Against Variants of Concern by COVID-19 mRNA Vaccines in Immunocompromised Patients

Didier Trono, Céline Pellaton, Madeleine Suffiotti

IMPORTANCE There are limited comparative data on the durability of neutralizing antibody (nAb) responses elicited by messenger RNA (mRNA) vaccines against the SARS-CoV-2 variants of concern (VOCs) in immunocompromised patients and healthy controls. ...

AMER MEDICAL ASSOC2022

Attribute Prediction as Multiple Instance Learning

Devis Tuia, Diego Marcos Gonzalez

Attribute-based representations help machine learning models perform tasks based on human understandable concepts, allowing a closer human-machine collaboration. However, learning attributes that accurately reflect the content of an image is not always str ...

2022

Integrative transcriptome analyses of Transposable Elements (TEs) and genes contribute to a better understanding of colorectal cancer

Eunji Shin

Cancer is the second leading cause of death worldwide. Cancer develops through multiple hallmark functions including apoptosis evasion, unlimited replicative potential, metastasis, and immune avoidance. Over the past few decades, researchers have reported ...

EPFL2022

Immunosuppressive roles of Activin-A in melanoma and characterization of its precursor cleavage

Katarina Pinjusic

Activin-A, a transforming growth factor ê µ family member, is a pleiotropic cytokine with diverse functions in development, fertility, adult tissue homeostasis, and aging. Accordingly, deregulation of Activin-A signaling has been associated with many patho ...

EPFL2022

Financial Risk Management with Machine Learning

Marc-Aurèle Antoine Divernois

This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...

EPFL2022

Weakly Supervised Volumetric Image Segmentation with Deformed Templates

Pascal Fua, Pamuditha Udaranga Wickramasinghe, Patrick Moller Jensen, Mian Akbar Shah

There are many approaches to weakly-supervised training of networks to segment 2D images. By contrast, existing approaches to segmenting volumetric images rely on full-supervision of a subset of 2D slices of the 3D volume. We propose an approach to volume ...

Springer2022

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