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In the field of computer aided medical image analysis, it is often difficult to obtain reliable ground truth for evaluating algorithms or supervising statistical learning procedures. In this paper we present a new method for training a classification forest from images labelled by variably performing experts, while simultaneously evaluating the performance(More)
Epithelial cells maintain an essential barrier despite continuously undergoing mitosis and apoptosis. Biological and biophysical mechanisms have evolved to remove dying cells while maintaining that barrier. Cell extrusion is thought to be driven by a multicellular filamentous actin ring formed by neighbouring cells, the contraction of which provides the(More)
In the context of forest-based segmentation of medical data, modeling the visual appearance around a voxel requires the choice of the scale at which contextual information is extracted, which is of crucial importance for the final segmentation performance. Building on Haar-like visual features, we introduce a simple yet effective modification of the forest(More)
Consider the following scenario between a human user and the computer. Given an image, the user thinks of an object to be segmented within this picture, but is only allowed to provide binary inputs to the computer (yes or no). In these conditions, can the computer guess this hidden segmentation by asking well-chosen questions to the user? We introduce a(More)
The large size of histological images combined with their very challenging appearance are two main difficulties which considerably complicate their analysis. In this paper, we introduce an interactive strategy leveraging the output of a supervised random forest classifier to guide a user through such large visual data. Starting from a forest-based pixelwise(More)
In this paper, we propose a supervised domain adaptation (DA) framework for adapting decision forests in the presence of distribution shift between training (source) and testing (target) domains, given few labeled examples. We introduce a novel method for DA through an error-correcting hierarchical transfer relaxation scheme with domain alignment, feature(More)
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. We formulate the prediction as a supervised regression task, with joint image descriptors as input and the output are the parameters of the transformation that guide the moving(More)
The examination of biopsy samples plays a central role in the diagnosis and staging of numerous diseases, including most cancer types. However, because of the large size of the acquired images, the localization and quantification of diseased portions of a tissue is usually time-consuming, as pathologists must scroll through the whole slide to look for(More)