469 citations · 1.6k across the 93 of their papers we have counts for
6 papers · 2 filters
Blind Inpainting of Large-scale Masks of Thin Structures with Adversarial and Reinforcement Learning
Hao Chen, Mario Valerio Giuffrida, Peter Doerner +1
Several imaging applications (vessels, retina, plant roots, road networks from satellites) require the accurate segmentation of thin structures for subsequent analysis. Discontinui…
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Agisilaos Chartsias, Giorgos Papanastasiou, Chengjia Wang +4
Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here…
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
Gabriele Valvano, Agisilaos Chartsias, Andrea Leo +1
There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whils…
FIRE: Unsupervised bi-directional inter-modality registration using deep networks
Chengjia Wang, Giorgos Papanastasiou, Agisilaos Chartsias +3
Inter-modality image registration is an critical preprocessing step for many applications within the routine clinical pathway. This paper presents an unsupervised deep inter-modali…
Disentangled Representation Learning in Cardiac Image Analysis
Agisilaos Chartsias, Thomas Joyce, Giorgos Papanastasiou +4
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Reson…
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization
Tian Xia, Agisilaos Chartsias, Sotirios A. Tsaftaris
Pseudo healthy synthesis, i.e. the creation of a subject-specific `healthy' image from a pathological one, could be helpful in tasks such as anomaly detection, understanding change…