Accurate and automatic organ segmentation is critical for computer-aided analysis towards
clinical decision support and treatment planning. State-of-the-art approaches have achieved …

In approaches for automatic localization of multiple anatomical landmarks, disambiguation
of locally similar structures as obtained by locally accurate candidate generation is often …

Purpose Toward an efficient clinical management of hepatocellular carcinoma (HCC), we
propose a classification framework dedicated to tumor necrosis rate estimation from dynamic …

State of the art anatomical landmark localization algorithms pair local Random Forest (RF)
detection with disambiguation of locally similar structures by including high level knowledge …

Microscopy image classification is important in various biomedical applications, such as
cancer subtype identification, and protein localization for high content screening. To achieve …

Whole body oncological screening using CT images requires a good anatomical localisation
of organs and the skeleton. While a number of algorithms for multi-organ localisation have …

In this paper, we address the multimodal registration problem from a novel perspective,
aiming to predict the transformation aligning images directly from their visual appearance …

Background Bioimage classification is a fundamental problem for many important biological
studies that require accurate cell phenotype recognition, subcellular localization, and …

We address the problem of multimodal image registration using a supervised learning
approach. We pose the problem as a regression task, whose goal is to estimate the …

Microscopy image classification is important in various biomedical applications, such as
cancer detection, subtype identification, and protein localization for high content screening …