677 citations · 1.6k across the 25 of their papers we have counts for
6 papers · 1 filter
A novel adversarial learning strategy for medical image classification
Zong Fan, Xiaohui Zhang, Jacob A. Gasienica +7
Deep learning (DL) techniques have been extensively utilized for medical image classification. Most DL-based classification networks are generally structured hierarchically and opt…
Evidence fusion with contextual discounting for multi-modality medical image segmentation
Ling Huang, Thierry Denoeux, Pierre Vera +1
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks. In this paper, we propose a new deep…
Application of belief functions to medical image segmentation: A review
Ling Huang, Su Ruan, Thierry Denoeux
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertaint…
A Quantitative Comparison between Shannon and Tsallis Havrda Charvat Entropies Applied to Cancer Outcome Prediction
Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera +1
In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for the training of a deep ne…
Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images
Amine Amyar, Romain Modzelewski, Pierre Vera +2
Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To that end, radiomics was proposed as a…
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
Ling Huang, Su Ruan, Pierre Decazes +1
An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (…